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A review on genetic algorithm: past, present, and future

Sourabh katoch.

Computer Science and Engineering Department, National Institute of Technology, Hamirpur, India

Sumit Singh Chauhan

Vijay kumar.

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

Introduction

In the recent years, metaheuristic algorithms are used to solve real-life complex problems arising from different fields such as economics, engineering, politics, management, and engineering [ 113 ]. Intensification and diversification are the key elements of metaheuristic algorithm. The proper balance between these elements are required to solve the real-life problem in an effective manner. Most of metaheuristic algorithms are inspired from biological evolution process, swarm behavior, and physics’ law [ 17 ]. These algorithms are broadly classified into two categories namely single solution and population based metaheuristic algorithm (Fig.  1 ). Single-solution based metaheuristic algorithms utilize single candidate solution and improve this solution by using local search. However, the solution obtained from single-solution based metaheuristics may stuck in local optima [ 112 ]. The well-known single-solution based metaheuristics are simulated annealing, tabu search (TS), microcanonical annealing (MA), and guided local search (GLS). Population-based metaheuristics utilizes multiple candidate solutions during the search process. These metaheuristics maintain the diversity in population and avoid the solutions are being stuck in local optima. Some of well-known population-based metaheuristic algorithms are genetic algorithm (GA) [ 135 ], particle swarm optimization (PSO) [ 101 ], ant colony optimization (ACO) [ 47 ], spotted hyena optimizer (SHO) [ 41 ], emperor penguin optimizer (EPO) [ 42 ], and seagull optimization (SOA) [ 43 ].

Classification of metaheuristic Algorithms

Among the metaheuristic algorithms, Genetic algorithm (GA) is a well-known algorithm, which is inspired from biological evolution process [ 136 ]. GA mimics the Darwinian theory of survival of fittest in nature. GA was proposed by J.H. Holland in 1992. The basic elements of GA are chromosome representation, fitness selection, and biological-inspired operators. Holland also introduced a novel element namely, Inversion that is generally used in implementations of GA [ 77 ]. Typically, the chromosomes take the binary string format. In chromosomes, each locus (specific position on chromosome) has two possible alleles (variant forms of genes) - 0 and 1. Chromosomes are considered as points in the solution space. These are processed using genetic operators by iteratively replacing its population. The fitness function is used to assign a value for all the chromosomes in the population [ 136 ]. The biological-inspired operators are selection, mutation, and crossover. In selection, the chromosomes are selected on the basis of its fitness value for further processing. In crossover operator, a random locus is chosen and it changes the subsequences between chromosomes to create off-springs. In mutation, some bits of the chromosomes will be randomly flipped on the basis of probability [ 77 , 135 , 136 ]. The further development of GA based on operators, representation, and fitness has diminished. Therefore, these elements of GA are focused in this paper.

The main contribution of this paper are as follows:

• The general framework of GA and hybrid GA are elaborated with mathematical formulation.
• The various types of genetic operators are discussed with their pros and cons.
• The variants of GA with their pros and cons are discussed.
• The applicability of GA in multimedia fields is discussed.

The main aim of this paper is two folds. First, it presents the variants of GA and their applicability in various fields. Second, it broadens the area of possible users in various fields. The various types of crossover, mutation, selection, and encoding techniques are discussed. The single-objective, multi-objective, parallel, and hybrid GAs are deliberated with their advantages and disadvantages. The multimedia applications of GAs are elaborated.

The remainder of this paper is organized as follows: Section 2 presents the methodology used to carry out the research. The classical genetic algorithm and genetic operators are discussed in Section 3 . The variants of genetic algorithm with pros and cons are presented in Section 4 . Section 5 describes the applications of genetic algorithm. Section 6 presents the challenges and future research directions. The concluding remarks are drawn in Section 7 .

Research methodology

PRISMA’s guidelines were used to conduct the review of GA [ 138 ]. A detailed search has been done on Google scholar and PubMed for identification of research papers related to GA. The important research works found during the manual search were also added in this paper. During search, some keywords such as “Genetic Algorithm” or “Application of GA” or “operators of GA” or “representation of GA” or “variants of GA” were used. The selection and rejection of explored research papers are based on the principles, which is mentioned in Table ​ Table1 1 .

Selection criterion for shortlisted research papers

Total 27,64,792 research papers were explored on Google Scholar, PubMed and manual search. The research work related to genetic algorithm for multimedia applications were also included. During the screening of research papers, all the duplicate papers and papers published before 2007 were discarded. 4340 research papers were selected based on 2007 and duplicate entries. Thereafter, 4050 research papers were eliminated based on titles. 220 research papers were eliminated after reading of abstract. 70 research papers were left after third round of screening. 40 more research papers were discarded after full paper reading and facts found in the papers. After the fourth round of screening, final 30 research papers are selected for review.

Based on the relevance and quality of research, 30 papers were selected for evaluation. The relevance of research is decided through some criteria, which is mentioned in Table ​ Table1. 1 . The selected research papers comprise of genetic algorithm for multimedia applications, advancement of their genetic operators, and hybridization of genetic algorithm with other well-established metaheuristic algorithms. The pros and cons of genetic operators are shown in preceding section.

In this section, the basic structure of GA and its genetic operators are discussed with pros and cons.

Classical GA

Genetic algorithm (GA) is an optimization algorithm that is inspired from the natural selection. It is a population based search algorithm, which utilizes the concept of survival of fittest [ 135 ]. The new populations are produced by iterative use of genetic operators on individuals present in the population. The chromosome representation, selection, crossover, mutation, and fitness function computation are the key elements of GA. The procedure of GA is as follows. A population ( Y ) of n chromosomes are initialized randomly. The fitness of each chromosome in Y is computed. Two chromosomes say C1 and C2 are selected from the population Y according to the fitness value. The single-point crossover operator with crossover probability (C p ) is applied on C1 and C2 to produce an offspring say O . Thereafter, uniform mutation operator is applied on produced offspring ( O ) with mutation probability (M p ) to generate O′ . The new offspring O′ is placed in new population. The selection, crossover, and mutation operations will be repeated on current population until the new population is complete. The mathematical analysis of GA is as follows [ 126 ]:

GA dynamically change the search process through the probabilities of crossover and mutation and reached to optimal solution. GA can modify the encoded genes. GA can evaluate multiple individuals and produce multiple optimal solutions. Hence, GA has better global search capability. The offspring produced from crossover of parent chromosomes is probable to abolish the admirable genetic schemas parent chromosomes and crossover formula is defined as [ 126 ]:

where g is the number of generations, and G is the total number of evolutionary generation set by population. It is observed from Eq.( 1 ) that R is dynamically changed and increase with increase in number of evolutionary generation. In initial stage of GA, the similarity between individuals is very low. The value of R should be low to ensure that the new population will not destroy the excellent genetic schema of individuals. At the end of evolution, the similarity between individuals is very high as well as the value of R should be high.

According to Schema theorem, the original schema has to be replaced with modified schema. To maintain the diversity in population, the new schema keep the initial population during the early stage of evolution. At the end of evolution, the appropriate schema will be produced to prevent any distortion of excellent genetic schema [ 65 , 75 ]. Algorithm 1 shows the pseudocode of classical genetic algorithm.

Algorithm 1: Classical Genetic Algorithm (GA)

Genetic operators

GAs used a variety of operators during the search process. These operators are encoding schemes, crossover, mutation, and selection. Figure ​ Figure2 2 depicts the operators used in GAs.

Operators used in GA

Encoding schemes

For most of the computational problems, the encoding scheme (i.e., to convert in particular form) plays an important role. The given information has to be encoded in a particular bit string [ 121 , 183 ]. The encoding schemes are differentiated according to the problem domain. The well-known encoding schemes are binary, octal, hexadecimal, permutation, value-based, and tree.

Binary encoding is the commonly used encoding scheme. Each gene or chromosome is represented as a string of 1 or 0 [ 187 ]. In binary encoding, each bit represents the characteristics of the solution. It provides faster implementation of crossover and mutation operators. However, it requires extra effort to convert into binary form and accuracy of algorithm depends upon the binary conversion. The bit stream is changed according the problem. Binary encoding scheme is not appropriate for some engineering design problems due to epistasis and natural representation.

In octal encoding scheme, the gene or chromosome is represented in the form of octal numbers (0–7). In hexadecimal encoding scheme, the gene or chromosome is represented in the form of hexadecimal numbers (0–9, A-F) [ 111 , 125 , 187 ]. The permutation encoding scheme is generally used in ordering problems. In this encoding scheme, the gene or chromosome is represented by the string of numbers that represents the position in a sequence. In value encoding scheme, the gene or chromosome is represented using string of some values. These values can be real, integer number, or character [ 57 ]. This encoding scheme can be helpful in solving the problems in which more complicated values are used. As binary encoding may fail in such problems. It is mainly used in neural networks for finding the optimal weights.

In tree encoding, the gene or chromosome is represented by a tree of functions or commands. These functions and commands can be related to any programming language. This is very much similar to the representation of repression in tree format [ 88 ]. This type of encoding is generally used in evolving programs or expressions. Table ​ Table2 2 shows the comparison of different encoding schemes of GA.

Comparison of different encoding schemes

Selection techniques

Selection is an important step in genetic algorithms that determines whether the particular string will participate in the reproduction process or not. The selection step is sometimes also known as the reproduction operator [ 57 , 88 ]. The convergence rate of GA depends upon the selection pressure. The well-known selection techniques are roulette wheel, rank, tournament, boltzmann, and stochastic universal sampling.

Roulette wheel selection maps all the possible strings onto a wheel with a portion of the wheel allocated to them according to their fitness value. This wheel is then rotated randomly to select specific solutions that will participate in formation of the next generation [ 88 ]. However, it suffers from many problems such as errors introduced by its stochastic nature. De Jong and Brindle modified the roulette wheel selection method to remove errors by introducing the concept of determinism in selection procedure. Rank selection is the modified form of Roulette wheel selection. It utilizes the ranks instead of fitness value. Ranks are given to them according to their fitness value so that each individual gets a chance of getting selected according to their ranks. Rank selection method reduces the chances of prematurely converging the solution to a local minima [ 88 ].

Tournament selection technique was first proposed by Brindle in 1983. The individuals are selected according to their fitness values from a stochastic roulette wheel in pairs. After selection, the individuals with higher fitness value are added to the pool of next generation [ 88 ]. In this method of selection, each individual is compared with all n-1 other individuals if it reaches the final population of solutions [ 88 ]. Stochastic universal sampling (SUS) is an extension to the existing roulette wheel selection method. It uses a random starting point in the list of individuals from a generation and selects the new individual at evenly spaced intervals [ 3 ]. It gives equal chance to all the individuals in getting selected for participating in crossover for the next generation. Although in case of Travelling Salesman Problem, SUS performs well but as the problem size increases, the traditional Roulette wheel selection performs relatively well [ 180 ].

Boltzmann selection is based on entropy and sampling methods, which are used in Monte Carlo Simulation. It helps in solving the problem of premature convergence [ 118 ]. The probability is very high for selecting the best string, while it executes in very less time. However, there is a possibility of information loss. It can be managed through elitism [ 175 ]. Elitism selection was proposed by K. D. Jong (1975) for improving the performance of Roulette wheel selection. It ensures the elitist individual in a generation is always propagated to the next generation. If the individual having the highest fitness value is not present in the next generation after normal selection procedure, then the elitist one is also included in the next generation automatically [ 88 ]. The comparison of above-mentioned selection techniques are depicted in Table ​ Table3 3 .

Comparison of different selection techniques

Crossover operators

Crossover operators are used to generate the offspring by combining the genetic information of two or more parents. The well-known crossover operators are single-point, two-point, k-point, uniform, partially matched, order, precedence preserving crossover, shuffle, reduced surrogate and cycle.

In a single point crossover, a random crossover point is selected. The genetic information of two parents which is beyond that point will be swapped with each other [ 190 ]. Figure ​ Figure3 3 shows the genetic information after swapping. It replaced the tail array bits of both the parents to get the new offspring.

Swapping genetic information after a crossover point

In a two point and k-point crossover, two or more random crossover points are selected and the genetic information of parents will be swapped as per the segments that have been created [ 190 ]. Figure ​ Figure4 4 shows the swapping of genetic information between crossover points. The middle segment of the parents is replaced to generate the new offspring.

Swapping genetic information between crossover points

In a uniform crossover, parent cannot be decomposed into segments. The parent can be treated as each gene separately. We randomly decide whether we need to swap the gene with the same location of another chromosome [ 190 ]. Figure ​ Figure5 5 depicts the swapping of individuals under uniform crossover operation.

Swapping individual genes

Partially matched crossover (PMX) is the most frequently used crossover operator. It is an operator that performs better than most of the other crossover operators. The partially matched (mapped) crossover was proposed by D. Goldberg and R. Lingle [ 66 ]. Two parents are choose for mating. One parent donates some part of genetic material and the corresponding part of other parent participates in the child. Once this process is completed, the left out alleles are copied from the second parent [ 83 ]. Figure ​ Figure6 6 depicts the example of PMX.

Partially matched crossover (PMX) [ 117 ]

Order crossover (OX) was proposed by Davis in 1985. OX copies one (or more) parts of parent to the offspring from the selected cut-points and fills the remaining space with values other than the ones included in the copied section. The variants of OX are proposed by different researchers for different type of problems. OX is useful for ordering problems [ 166 ]. However, it is found that OX is less efficient in case of Travelling Salesman Problem [ 140 ]. Precedence preserving crossover (PPX) preserves the ordering of individual solutions as present in the parent of offspring before the application of crossover. The offspring is initialized to a string of random 1’s and 0’s that decides whether the individuals from both parents are to be selected or not. In [ 169 ], authors proposed a modified version of PPX for multi-objective scheduling problems.

Shuffle crossover was proposed by Eshelman et al. [ 20 ] to reduce the bias introduced by other crossover techniques. It shuffles the values of an individual solution before the crossover and unshuffles them after crossover operation is performed so that the crossover point does not introduce any bias in crossover. However, the utilization of this crossover is very limited in the recent years. Reduced surrogate crossover (RCX) reduces the unnecessary crossovers if the parents have the same gene sequence for solution representations [ 20 , 139 ]. RCX is based on the assumption that GA produces better individuals if the parents are sufficiently diverse in their genetic composition. However, RCX cannot produce better individuals for those parents that have same composition. Cycle crossover was proposed by Oliver [ 140 ]. It attempts to generate an offspring using parents where each element occupies the position by referring to the position of their parents [ 140 ]. In the first cycle, it takes some elements from the first parent. In the second cycle, it takes the remaining elements from the second parent as shown in Fig.  7 .

Cycle Crossover (CX) [ 140 ]

Table ​ Table4 4 shows the comparison of crossover techniques. It is observed from Table ​ Table4 4 that single and k-point crossover techniques are easy to implement. Uniform crossover is suitable for large subsets. Order and cycle crossovers provide better exploration than the other crossover techniques. Partially matched crossover provides better exploration. The performance of partially matched crossover is better than the other crossover techniques. Reduced surrogate and cycle crossovers suffer from premature convergence.

Comparison of different crossover techniques

Mutation operators

Mutation is an operator that maintains the genetic diversity from one population to the next population. The well-known mutation operators are displacement, simple inversion, and scramble mutation. Displacement mutation (DM) operator displaces a substring of a given individual solution within itself. The place is randomly chosen from the given substring for displacement such that the resulting solution is valid as well as a random displacement mutation. There are variants of DM are exchange mutation and insertion mutation. In Exchange mutation and insertion mutation operators, a part of an individual solution is either exchanged with another part or inserted in another location, respectively [ 88 ].

The simple inversion mutation operator (SIM) reverses the substring between any two specified locations in an individual solution. SIM is an inversion operator that reverses the randomly selected string and places it at a random location [ 88 ]. The scramble mutation (SM) operator places the elements in a specified range of the individual solution in a random order and checks whether the fitness value of the recently generated solution is improved or not [ 88 ]. Table ​ Table5 5 shows the comparison of different mutation techniques.

Comparison of different mutation operators

Table ​ Table6 6 shows the best combination of encoding scheme, mutation, and crossover techniques. It is observed from Table ​ Table6 6 that uniform and single-point crossovers can be used with most of encoding and mutation operators. Partially matched crossover is used with inversion mutation and permutation encoding scheme provides the optimal solution.

Best combination of various operators under optimal Environment

Variants of GA

Various variants of GA’s have been proposed by researchers. The variants of GA are broadly classified into five main categories namely, real and binary coded, multiobjective, parallel, chaotic, and hybrid GAs. The pros and cons of these algorithms with their application has been discussed in the preceding subsections.

Real and binary coded GAs

Based on the representation of chromosomes, GAs are categorized in two classes, namely binary and real coded GAs.

Binary coded GAs

The binary representation was used to encode GA and known as binary GA. The genetic operators were also modified to carry out the search process. Payne and Glen [ 153 ] developed a binary GA to identify the similarity among molecules. They used binary representation for position of molecule and their conformations. However, this method has high computational complexity. Longyan et al. [ 203 ] investigated three different method for wind farm design using binary GA (BGA). Their method produced better fitness value and farm efficiency. Shukla et al. [ 185 ] utilized BGA for feature subset selection. They used mutual information maximization concept for selecting the significant features. BGAs suffer from Hamming cliffs, uneven schema, and difficulty in achieving precision [ 116 , 199 ].

Real-coded GAs

Real-coded GAs (RGAs) have been widely used in various real-life applications. The representation of chromosomes is closely associated with real-life problems. The main advantages of RGAs are robust, efficient, and accurate. However, RGAs suffer from premature convergence. Researchers are working on RGAs to improve their performance. Most of RGAs are developed by modifying the crossover, mutation and selection operators.

The searching capability of crossover operators are not satisfactory for continuous search space. The developments in crossover operators have been done to enhance their performance in real environment. Wright [ 210 ] presented a heuristics crossover that was applied on parents to produce off-spring. Michalewicz [ 135 ] proposed arithmetical crossover operators for RGAs. Deb and Agrawal [ 34 ] developed a real-coded crossover operator, which is based on characteristics of single-point crossover in BGA. The developed crossover operator named as simulated binary crossover (SBX). SBX is able to overcome the Hamming cliff, precision, and fixed mapping problem. The performance of SBX is not satisfactory in two-variable blocked function. Eshelman et al. [ 53 ] utilized the schemata concept to design the blend crossover for RGAs. The unimodal normal distribution crossover operator (UNDX) was developed by Ono et al. [ 144 ]. They used ellipsoidal probability distribution to generate the offspring. Kita et al. [ 106 ] presented a multi-parent UNDX (MP-UNDX), which is the extension of [ 144 ]. However, the performance of RGA with MP-UNDX is much similar to UNDX. Deep and Thakur [ 39 ] presented a Laplace crossover for RGAs, which is based on Laplacian distribution. Chuang et al. [ 27 ] developed a direction based crossover to further explore the all possible search directions. However, the search directions are limited. The heuristic normal distribution crossover operator was developed by Wang et al. [ 207 ]. It generates the cross-generated offspring for better search operation. However, the better individuals are not considered in this approach. Subbaraj et al. [ 192 ] proposed Taguchi self-adaptive RCGA. They used Taguchi method and simulated binary crossover to exploit the capable offspring.

Mutation operators generate diversity in the population. The two main challenges have to tackle during the application of mutation. First, the probability of mutation operator that was applied on population. Second, the outlier produced in chromosome after mutation process. Michalewicz [ 135 ] presented uniform and non-uniform mutation operators for RGAs. Michalewicz and Schoenauer [ 136 ] developed a special case of uniform mutation. They developed boundary mutation. Deep and Thakur [ 38 ] presented a novel mutation operator based on power law and named as power mutation. Das and Pratihar [ 30 ] presented direction-based exponential mutation operator. They used direction information of variables. Tang and Tseng [ 196 ] presented a novel mutation operator for enhancing the performance of RCGA. Their approach was fast and reliable. However, it stuck in local optima for some applications. Deb et al. [ 35 ] developed polynomial mutation that was used in RCGA. It provides better exploration. However, the convergence speed is slow and stuck in local optima. Lucasius et al. [ 129 ] proposed a real-coded genetic algorithm (RCGA). It is simple and easy to implement. However, it suffers from local optima problem. Wang et al. [ 205 ] developed multi-offspring GA and investigated their performance over single point crossover. Wang et al. [ 206 ] stated the theoretical basis of multi-offspring GA. The performance of this method is better than non-multi-offspring GA. Pattanaik et al. [ 152 ] presented an improvement in the RCGA. Their method has better convergence speed and quality of solution. Wang et al. [ 208 ] proposed multi-offspring RCGA with direction based crossover for solving constrained problems.

Table ​ Table7 7 shows the mathematical formulation of genetic operators in RGAs.

Mathematical formulation of genetic operators in RGAs

Multiobjective GAs

Multiobjective GA (MOGA) is the modified version of simple GA. MOGA differ from GA in terms of fitness function assignment. The remaining steps are similar to GA. The main motive of multiobjective GA is to generate the optimal Pareto Front in the objective space in such a way that no further enhancement in any fitness function without disturbing the other fitness functions [ 123 ]. Convergence, diversity, and coverage are main goal of multiobjective GAs. The multiobjective GAs are broadly categorized into two categories namely, Pareto-based, and decomposition-based multiobjective GAs [ 52 ]. These techniques are discussed in the preceding subsections.

Pareto-based multi-objective GA

The concept of Pareto dominance was introduced in multiobjective GAs. Fonseca and Fleming [ 56 ] developed first multiobjective GA (MOGA). The niche and decision maker concepts were proposed to tackle the multimodal problems. However, MOGA suffers from parameter tuning problem and degree of selection pressure. Horn et al. [ 80 ] proposed a niched Pareto genetic algorithm (NPGA) that utilized the concept of tournament selection and Pareto dominance. Srinivas and Deb [ 191 ] developed a non-dominated sorting genetic algorithm (NSGA). However, it suffers from lack of elitism, need of sharing parameter, and high computation complexity. To alleviate these problems, Deb et al. [ 36 ] developed a fast elitist non-dominated sorting genetic algorithm (NSGA-II). The performance of NSGA-II may be deteriorated for many objective problems. NSGA-II was unable to maintain the diversity in Pareto-front. To alleviate this problem, Luo et al. [ 130 ] introduced a dynamic crowding distance in NSGA-II. Coello and Pulido [ 28 ] developed a multiobjective micro GA. They used an archive for storing the non-dominated solutions. The performance of Pareto-based approaches may be deteriorated in many objective problems [ 52 ].

Decomposition-based multiobjective GA

Decomposition-based MOGAs decompose the given problem into multiple subproblems. These subproblems are solved simultaneously and exchange the solutions among neighboring subproblems [ 52 ]. Ishibuchi and Murata [ 84 ] developed a multiobjective genetic local search (MOGLS). In MOGLS, the random weights were used to select the parents and local search for their offspring. They used generation replacement and roulette wheel selection method. Jaszkiewicz [ 86 ] modified the MOGLS by utilizing different selection mechanisms for parents. Murata and Gen [ 141 ] proposed a cellular genetic algorithm for multiobjective optimization (C-MOGA) that was an extension of MOGA. They added cellular structure in MOGA. In C-MOGA, the selection operator was performed on the neighboring of each cell. C-MOGA was further extended by introducing an immigration procedure and known as CI-MOGA. Alves and Almeida [ 11 ] developed a multiobjective Tchebycheffs-based genetic algorithm (MOTGA) that ensures convergence and diversity. Tchebycheff scalar function was used to generate non-dominated solution set. Patel et al. [ 151 ] proposed a decomposition based MOGA (D-MOGA). They integrated opposition based learning in D-MOGA for weight vector generation. D-MOGA is able to maintain the balance between diversity of solutions and exploration of search space.

Parallel GAs

The motivation behind the parallel GAs is to improve the computational time and quality of solutions through distributed individuals. Parallel GAs are categorized into three broad categories such as master-slave parallel GAs, fine grained parallel GAs, and multi-population coarse grained parallel Gas [ 70 ]. In master-slave parallel GA, the computation of fitness functions is distributed over the several processors. In fine grained GA, parallel computers are used to solve the real-life problems. The genetic operators are bounded to their neighborhood. However, the interaction is allowed among the individuals. In coarse grained GA, the exchange of individuals among sub-populations is performed. The control parameters are also transferred during migration. The main challenges in parallel GAs are to maximize memory bandwidth and arrange threads for utilizing the power of GPUs [ 23 ]. Table ​ Table8 8 shows the comparative analysis of parallel GAs in terms of hardware and software. The well-known parallel GAs are studied in the preceding subsections.

Analysis of parallel GAs in terms of hardware and software

Master slave parallel GA

The large number of processors are utilized in master-slave parallel GA (MS-PGA) as compared to other approaches. The computation of fitness functions may be increased by increasing the number of processors. Hong et al. [ 79 ] used MS-PGA for solving data mining problems. Fuzzy rules are used with parallel GA. The evaluation of fitness function was performed on slave machines. However, it suffers from high computational time. Sahingzo [ 174 ] implemented MS-PGA for UAV path finding problem. The genetic operators were executed on processors. They used multicore CPU with four cores. Selection and fitness evaluation was done on slave machines. MS-PGA was applied on traffic assignment problem in [ 127 ]. They used thirty processors to solve this problem at National University of Singapore. Yang et al. [ 213 ] developed a web-based parallel GA. They implemented the master slave version of NSGA-II in distributed environment. However, the system is complex in nature.

Fine grained parallel GA

In last few decades, researchers are working on migration policies of fine grained parallel GA (FG-PGA). Porta et al. [ 161 ] utilized clock-time for migration frequency, which is independent of generations. They used non-uniform structure and static configuration. The best solution was selected for migration and worst solution was replaced with migrant solution. Kurdi [ 115 ] used adaptive migration frequency. The migration procedure starts until there is no change in the obtained solutions after ten successive generations. The non-uniform and dynamic structure was used. In [ 209 ], local best solutions were synchronized and formed a global best solutions. The global best solutions were transferred to all processors for father execution. The migration frequency depends upon the number of generation. They used uniform structure with fixed configuration. Zhang et al. [ 220 ] used parallel GA to solve the set cover problem of wireless networks. They used divide-and-conquer strategy to decompose the population into sub-populations. Thereafter, the genetic operators were applied on local solutions and Kuhn-Munkres was used to merge the local solutions.

Coarse grained parallel GA

Pinel et al. [ 158 ] proposed a GraphCell. The population was initialized with random values and one solution was initialized with Min-min heuristic technique. 448 processors were used to implement the proposed approach. However, coarse grained parallel GAs are less used due to complex in nature. The hybrid parallel GAs are widely used in various applications. Shayeghi et al. [ 182 ] proposed a pool-based Birmingham cluster GA. Master node was responsible for managing global population. Slave node selected the solutions from global population and executed it. 240 processors are used for computation. Roberge et al. [ 170 ] used hybrid approach to optimize switching angle of inverters. They used four different strategies for fitness function computation. Nowadays, GPU, cloud, and grid are most popular hardware for parallel GAs [ 198 ].

Chaotic GAs

The main drawback of GAs is premature convergence. The chaotic systems are incorporated into GAs to alleviate this problem. The diversity of chaos genetic algorithm removes premature convergence. Crossover and mutation operators can be replaced with chaotic maps. Tiong et al. [ 197 ] integrated the chaotic maps into GA for further improvement in accuracy. They used six different chaotic maps. The performance of Logistic, Henon and Ikeda chaotic GA performed better than the classical GA. However, these techniques suffer from high computational complexity. Ebrahimzadeh and Jampour [ 48 ] used Lorenz chaotic for genetic operators of GA to eliminate the local optima problem. However, the proposed approach was unable to find relationship between entropy and chaotic map. Javidi and Hosseinpourfard [ 87 ] utilized two chaotic maps namely logistic map and tent map for generating chaotic values instead of random selection of initial population. The proposed chaotic GA performs better than the GA. However, this method suffers from high computational complexity. Fuertes et al. [ 60 ] integrated the entropy into chaotic GA. The control parameters are modified through chaotic maps. They investigated the relationship between entropy and performance optimization.

Chaotic systems have also used in multiobjective and hybrid GAs. Abo-Elnaga and Nasr [ 5 ] integrated chaotic system into modified GA for solving Bi-level programming problems. Chaotic helps the proposed algorithm to alleviate local optima and enhance the convergence. Tahir et al. [ 193 ] presented a binary chaotic GA for feature selection in healthcare. The chaotic maps were used to initialize the population and modified reproduction operators were applied on population. Xu et al. [ 115 ] proposed a chaotic hybrid immune GA for spectrum allocation. The proposed approach utilizes the advantages of both chaotic and immune operator. However, this method suffers from parameter initialization problem.

Genetic Algorithms can be easily hybridized with other optimization methods for improving their performance such as image denoising methods, chemical reaction optimization, and many more. The main advantages of hybridized GA with other methods are better solution quality, better efficiency, guarantee of feasible solutions, and optimized control parameters [ 51 ]. It is observed from literature that the sampling capability of GAs is greatly affected from population size. To resolve this problem, local search algorithms such as memetic algorithm, Baldwinian, Lamarckian, and local search have been integrated with GAs. This integration provides proper balance between intensification and diversification. Another problem in GA is parameter setting. Finding appropriate control parameters is a tedious task. The other metaheuristic techniques can be used with GA to resolve this problem. Hybrid GAs have been used to solve the issues mentioned in the preceding subsections [ 29 , 137 , 186 ].

Enhance search capability

GAs have been integrated with local search algorithms to reduce the genetic drift. The explicit refinement operator was introduced in local search for producing better solutions. El-Mihoub et al. [ 54 ] established the effect of probability of local search on the population size of GA. Espinoza et al. [ 50 ] investigated the effect of local search for reducing the population size of GA. Different search algorithms have been integrated with GAs for solving real-life applications.

Generate feasible solutions

In complex and high-dimensional problems, the genetic operators of GA generate infeasible solutions. PMX crossover generates the infeasible solutions for order-based problems. The distance preserving crossover operator was developed to generate feasible solutions for travelling salesman problem [ 58 ]. The gene pooling operator instead of crossover was used to generate feasible solution for data clustering [ 19 ]. Konak and Smith [ 108 ] integrated a cut-saturation algorithm with GA for designing the communication networks. They used uniform crossover to produce feasible solutions.

Replacement of genetic operators

There is a possibility to replace the genetic operators which are mentioned in Section 3.2 with other search techniques. Leng [ 122 ] developed a guided GA that utilizes the penalties from guided local search. These penalties were used in fitness function to improve the performance of GA. Headar and Fukushima [ 74 ] used simplex crossover instead of standard crossover. The standard mutation operator was replaced with simulated annealing in [ 195 ]. The basic concepts of quantum computing are used to improve the performance of GAs. The heuristic crossover and hill-climbing operators can be integrated into GA for solving three-matching problem.

Optimize control parameters

The control parameters of GA play a crucial role in maintaining the balance between intensification and diversification. Fuzzy logic has an ability to estimate the appropriate control parameters of GA [ 167 ]. Beside this, GA can be used to optimize the control parameters of other techniques. GAs have been used to optimize the learning rate, weights, and topology of neutral networks [ 21 ]. GAs can be used to estimate the optimal value of fuzzy membership in controller. It was also used to optimize the control parameters of ACO, PSO, and other metaheuristic techniques [ 156 ]. The comparative analysis of well-known GAs are mentioned in Table ​ Table9 9 .

Comparative study of GA’s variants in terms of pros and cons

Applications

Genetic Algorithms have been applied in various NP-hard problems with high accuracy rates. There are a few application areas in which GAs have been successfully applied.

Operation management

GA is an efficient metaheuristic for solving operation management (OM) problems such as facility layout problem (FLP), supply network design, scheduling, forecasting, and inventory control.

Facility layout

Datta et al. [ 32 ] utilized GA for solving single row facility layout problem (SRFLP). For SRFLP, the modified crossover and mutation operators of GA produce valid solutions. They applied GA to large sized problems that consists of 60–80 instances. However, it suffers from parameter dependency problem. Sadrzadeh [ 173 ] proposed GA for multi-line FLP have multi products. The facilities were clustered using mutation and heuristic operators. The total cost obtained from the proposed GA was decreased by 7.2% as compared to the other algorithms. Wu et al. [ 211 ] implemented hierarchical GA to find out the layout of cellular manufacturing system. However, the performance of GA is greatly affected from the genetic operators. Aiello et al. [ 7 ] proposed MOGA for FLP. They used MOGA on the layout of twenty different departments. Palomo-Romero et al. [ 148 ] proposed an island model GA to solve the FLP. The proposed technique maintains the population diversity and generates better solutions than the existing techniques. However, this technique suffers from improper migration strategy that can be utilized for improving the population. GA and its variants has been successfully applied on FLP [ 103 , 119 , 133 , 201 ].

GA shows the superior performance for solving the scheduling problems such as job-shop scheduling (JSS), integrated process planning and scheduling (IPPS), etc. [ 119 ]. To improve the performance in the above-mentioned areas of scheduling, researchers developed various genetic representation [ 12 , 159 , 215 ], genetic operators, and hybridized GA with other methods [ 2 , 67 , 147 , 219 ].

Inventory control

Besides the scheduling, inventory control plays an important role in OM. Backordering and lost sales are two main approaches for inventory control [ 119 ]. Hiassat et al. [ 76 ] utilized the location-inventory model to find out the number and location of warehouses. Various design constraints have been added in the objective functions of GA and its variants for solving inventory control problem [].

Forecasting and network design

Forecasting is an important component for OM. Researchers are working on forecasting of financial trading, logistics demand, and tourist arrivals. GA has been hybridized with support vector regression, fuzzy set, and neural network (NN) to improve their forecasting capability [ 22 , 78 , 89 , 178 , 214 ]. Supply network design greatly affect the operations planning and scheduling. Most of the research articles are focused on capacity constraints of facilities [ 45 , 184 ]. Multi-product multi-period problems increases the complexity of supply networks. To resolve the above-mentioned problem, GA has been hybridized with other techniques [ 6 , 45 , 55 , 188 , 189 ]. Multi-objective GAs are also used to optimize the cost, profit, carbon emissions, etc. [ 184 , 189 ].

GAs have been applied in various fields of multimedia. Some of well-known multimedia fields are encryption, image processing, video processing, medical imaging, and gaming.

Information security

Due to development in multimedia applications, images, videos and audios are transferred from one place to another over Internet. It has been found in literature that the images are more error prone during the transmission. Therefore, image protection techniques such as encryption, watermarking and cryptography are required. The classical image encryption techniques require the input parameters for encryption. The wrong selection of input parameters will generate inadequate encryption results. GA and its variants have been used to select the appropriate control parameters. Kaur and Kumar [ 96 ] developed a multi-objective genetic algorithm to optimize the control parameters of chaotic map. The secret key was generated using beta chaotic map. The generated key was use to encrypt the image. Parallel GAs were also used to encrypt the image [ 97 ].

Image processing

The main image processing tasks are preprocessing, segmentation, object detection, denoising, and recognition. Image segmentation is an important step to solve the image processing problems. Decomposing/partitioning an image requires high computational time. To resolve this problem, GA is used due to their better search capability [ 26 , 102 ]. Enhancement is a technique to improve the quality and contrast of an image. The better image quality is required to analyze the given image. GAs have been used to enhance natural contrast and magnify image [ 40 , 64 , 99 ]. Some researchers are working on hybridization of rough set with adaptive genetic algorithm to merge the noise and color attributes. GAs have been used to remove the noise from the given image. GA can be hybridized with fuzzy logic to denoise the noisy image. GA based restoration technique can be used to remove haze, fog and smog from the given image [ 8 , 110 , 146 , 200 ]. Object detection and recognition is a challenging issue in real-world problem. Gaussian mixture model provides better performance during detection and recognition process. The control parameters are optimized through GA [ 93 ].

Video processing

Video segmentation has been widely used in pattern recognition, and computer vision. There are some critical issues that are associated with video segmentation. These are distinguishing object from the background and determine accurate boundaries. GA can be used to resolve these issues [ 9 , 105 ]. GAs have been implemented for gesture recognition successfully by Chao el al. [ 81 ] used GA for gesture recognition. They applied GAs and found an accuracy of 95% in robot vision. Kaluri and Reddy [ 91 ] proposed an adaptive genetic algorithm based method along with fuzzy classifiers for sign gesture recognition. They reported an improved recognition rate of 85% as compared to the existing method that provides 79% accuracy. Beside the gesture recognition, face recognition play an important role in criminal identification, unmanned vehicles, surveillance, and robots. GA is able to tackle the occlusion, orientations, expressions, pose, and lighting condition [ 69 , 95 , 109 ].

Medical imaging

Genetic algorithms have been applied in medical imaging such as edge detection in MRI and pulmonary nodules detection in CT scan images [ 100 , 179 ]. In [ 120 ], authors used a template matching technique with GA for detecting nodules in CT images. Kavitha and Chellamuthu [ 179 ] used GA based region growing method for detecting the brain tumor. GAs have been applied on medical prediction problems captured from pathological subjects. Sari and Tuna [ 176 ] used GA used to solve issues arises in biomechanics. It is used to predict pathologies during examination. Ghosh and Bhattachrya [ 62 ] implemented sequential GA with cellular automata for modelling the coronavirus disease 19 (COVID-19) data. GAs can be applied in parallel mode to find rules in biological datasets [ 31 ]. The authors proposed a parallel GA that runs by dividing the process into small sub-generations and evaluating the fitness of each individual solution in parallel. Genetic algorithms are used in medicine and other related fields. Koh et al. [ 61 ] proposed a genetic algorithm based method for evaluation of adverse effects of a given drug.

Precision agriculture

GAs have been applied on various problems that are related to precision agriculture. The main issues are crop yield, weed detection, and improvement in farming equipment. Pachepsky and Acock [ 145 ] implemented GA to analyze the water capacity in soil using remote sensing images. The crop yield can be predicted through the capacity of water present in soil. The weed identification was done through GA in [ 142 ]. They used aerial image for classification of plants. In [ 124 ], color image segmentation was used to discriminate the weed and plant. Peerlink et al. [ 154 ] determined the appropriate rate of fertilizer for various portions of agriculture field. They GA for determining the nitrogen in wheat field. The energy requirements in water irrigation systems can be optimized by viewing it as a multi-objective optimization problem. The amount of irrigation required and thus power requirements change continuously in a SMART farm. Therefore, GA can be applied in irrigation systems to reduce the power requirements [ 33 ].

GAs have been successfully used in games such as gomoku. In [ 202 ], the authors shown that the GA based approach finds the solution having the highest fitness than the normal tree based methods. However, in real-time strategy based games, GA based solutions become less practical to implement [ 82 ]. GAs have been implemented for path planning problems considering the environment constraints as well as avoiding the obstacles to reach the given destination. Burchardt and Salomon [ 18 ] described an implementation for path planning for soccer games. GA can encode the path planning problems via the coordinate points of a two-dimensional playing field, hence resulting in a variable length solution. The fitness function in path planning considers length of path as well as the collision avoiding terms for soccer players.

Wireless networking

Due to adaptive, scalable, and easy implementation of GA, it has been used to solve the various issues of wireless networking. The main issues of wireless networking are routing, quality of service, load balancing, localization, bandwidth allocation and channel assignment [ 128 , 134 ]. GA has been hybridized with other metaheuristics for solving the routing problems. Hybrid GA not only producing the efficient routes among pair of nodes, but also used for load balancing [ 24 , 212 ].

Load balancing

Nowadays, multimedia applications require Quality-of-Service (QoS) demand for delay and bandwidth. Various researchers are working on GAs for QoS based solutions.GA produces optimal solutions for complex networks [ 49 ]. Roy et al. [ 172 ] proposed a multi-objective GA for multicast QoS routing problem. GA was used with ACO and other search algorithms for finding optimal routes with desired QoS metrics. Load balancing is another issue in wireless networks. Scully and Brown [ 177 ] used MicroGAs and MacroGAs to distribute the load among various components of networks. He et al. [ 73 ] implemented GA to determine the balance load in wireless sensor networks. Cheng et al. [ 25 ] utilized distributed GA with multi-population scheme for load balancing. They used load balancing metric as a fitness function in GA.

Localization

The process of determining the location of wireless nodes is called as localization. It plays an important role in disaster management and military services. Yun et al. [ 216 ] used GA with fuzzy logic to find out the weights, which are assigned according to the signal strength. Zhang et al. [ 218 ] hybridized GA with simulated annealing (SA) to determine the position of wireless nodes. SA is used as local search to eliminate the premature convergence.

Bandwidth and channel allocation

The appropriate bandwidth allocation is a complex task. GAs and its variants have been developed to solve the bandwidth allocation problem [ 92 , 94 , 107 ]. GAs were used to investigate the allocation of bandwidth with QoS constraints. The fitness function of GAs may consists of resource utilization, bandwidth distribution, and computation time [ 168 ]. The channel allocation is an important issue in wireless networks. The main objective of channel allocation is to simultaneously optimize the number of channels and reuse of allocated frequency. Friend et al. [ 59 ] used distributed island GA to resolve the channel allocation problem in cognitive radio networks. Zhenhua et al. [ 221 ] implemented a modified immune GA for channel assignment. They used different encoding scheme and immune operators. Pinagapany and Kulkarni [ 157 ] developed a parallel GA to solve both static and dynamic channel allocation problem. They used decimal encoding scheme. Table ​ Table10 10 summarizes the applications of GA and its variants.

Applications of GA

Challenges and future possibilities

In this section, the main challenges faced during the implementation of GAs are discussed followed by the possible research directions.

Despite the several advantages, there are some challenges that need to be resolved for future advancements and further evolution of genetic algorithms. Some major challenges are given below:

Selection of initial population

Initial population is always considered as an important factor for the performance of genetic algorithms. The size of population also affects the quality of solution [ 160 ]. The researchers argue that if a large population is considered, then the algorithm takes more computation time. However, the small population may lead to poor solution [ 155 ]. Therefore, finding the appropriate population size is always a challenging issue. Harik and Lobo [ 71 ] investigated the population using self-adaption method. They used two approaches such as (1) use of self-adaption prior to execution of algorithm, in which the size of population remains the same and (2) in which the self-adaption used during the algorithm execution where the population size is affected by fitness function.

Premature convergence

Premature convergence is a common issue for GA. It can lead to the loss of alleles that makes it difficult to identify a gene [ 15 ]. Premature convergence states that the result will be suboptimal if the optimization problem coincides too early. To avoid this issue, some researchers suggested that the diversity should be used. The selection pressure should be used to increase the diversity. Selection pressure is a degree which favors the better individuals in the initial population of GA’s. If selection pressure (SP1) is greater than some selection pressure (SP2), then population using SP1 should be larger than the population using SP2. The higher selection pressure can decrease the population diversity that may lead to premature convergence [ 71 ].

Convergence property has to be handled properly so that the algorithm finds global optimal solution instead of local optimal solution (see Fig. ​ Fig.8). 8 ). If the optimal solution lies in the vicinity of an infeasible solution, then the global nature of GA can be combined with local nature of other algorithms such as Tabu search and local search. The global nature of genetic algorithms and local nature of Tabu search provide the proper balance between intensification and diversification.

Local and global optima [ 149 ]

Selection of efficient fitness functions

Fitness function is the driving force, which plays an important role in selecting the fittest individual in every iteration of an algorithm. If the number of iterations are small, then a costly fitness function can be adjusted. The number of iterations increases may increase the computational cost. The selection of fitness function depends upon the computational cost as well as their suitability. In [ 46 ], the authors used Davies-Bouldin index for classification of documents.

Degree of mutation and crossover

Crossover and mutation operators are the integral part of GAs. If the mutation is not considered during evolution, then there will be no new information available for evolution. If crossover is not considered during evolution, then the algorithm can result in local optima. The degree of these operators greatly affect the performance of GAs [ 72 ]. The proper balance between these operators are required to ensure the global optima. The probabilistic nature cannot determine the exact degree for an effective and optimal solution.

Selection of encoding schemes

GAs require a particular encoding scheme for a specific problem. There is no general methodology for deciding whether the particular encoding scheme is suitable for any type of real-life problem. If there are two different problems, then two different encoding schemes are required. Ronald [ 171 ] suggested that the encoding schemes should be designed to overwhelm the redundant forms. The genetic operators should be implemented in a manner that they are not biased towards the redundant forms.

Future research directions

GAs have been applied in different fields by modifying the basic structure of GA. The optimality of a solution obtained from GA can be made better by overcoming the current challenges. Some future possibilities for GA are as follows:

• There should be some way to choose the appropriate degree of crossover and mutation operators. For example Self-Organizing GA adapt the crossover and mutation operators according to the given problem. It can save computation time that make it faster.
• Future work can also be considered for reducing premature convergence problem. Some researchers are working in this direction. However, it is suggested that new methods of crossover and mutation techniques are required to tackle the premature convergence problem.
• Genetic algorithms mimic the natural evolution process. There can be a possible scope for simulating the natural evolution process such as the responses of human immune system and the mutations in viruses.
• In real-life problems, the mapping from genotype to phenotype is complex. In this situation, the problem has no obvious building blocks or building blocks are not adjacent groups of genes. Hence, there is a possibility to develop novel encoding schemes to different problems that does not exhibit same degree of difficulty.

Conclusions

This paper presents the structured and explained view of genetic algorithms. GA and its variants have been discussed with application. Application specific genetic operators are discussed. Some genetic operators are designed for representation. However, they are not applicable to research domains. The role of genetic operators such as crossover, mutation, and selection in alleviating the premature convergence is studied extensively. The applicability of GA and its variants in various research domain has been discussed. Multimedia and wireless network applications were the main attention of this paper. The challenges and issues mentioned in this paper will help the practitioners to carry out their research. There are many advantages of using GAs in other research domains and metaheuristic algorithms.

The intention of this paper is not only provide the source of recent research in GAs, but also provide the information about each component of GA. It will encourage the researchers to understand the fundamentals of GA and use the knowledge in their research problems.

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Evolutionary algorithms and their applications to engineering problems

• Review Article
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• Published: 16 March 2020
• Volume 32 , pages 12363–12379, ( 2020 )

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• Adam Slowik 1 &
• Halina Kwasnicka 2  

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The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.

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1 Introduction

These days, in the area of soft computing research we can observe a strong pressure to search for new optimization techniques which are based on nature. Figure  1 presents some approaches in optimization techniques with a concentration on evolutionary approaches. Today, the whole family of evolutionary optimization algorithms is referred to as evolutionary computation (EC) algorithms. In the evolutionary computation domain, we can mention the following main algorithms: the genetic algorithm (GA) [ 1 ], genetic programming (GP) [ 2 ], differential evolution (DE) [ 3 ], the evolution strategy (ES) [ 4 ], and evolutionary programming (EP) [ 5 ]. Each of these techniques has many different varieties and is used in many different industrial applications.

Taxonomy of nature-inspired methods

This paper is a state-of-the-art paper which topic is connected mainly with evolutionary algorithms (EAs) such as GA, GP, DE, ES, and EP. (In the other paper [ 6 ], we have presented swarm intelligence algorithms (SIAs) such as ant colony optimization (ACO), particle swarm optimization (PSO), and others in which social collaboration between agents exist.) The other nature-based methods, like family of physical algorithms (e.g., simulated annealing, extremal optimization, harmony search, cultural algorithm, gravitational search, river formation dynamics, black hole algorithm), or family of plant intelligence algorithms (e.g., flower pollination algorithm, invasive weed optimization, paddy field algorithm, artificial plant optimization algorithm, photosynthetic algorithm, plant growth optimization, rooted tree optimization), are not considered here due to their less popularity.

The aim of this paper is to present a short overview of the practical applications of evolutionary algorithms (EAs). The paper is the complement to [ 6 ] where a state of the art of industrial (real-life) applications of swarm intelligence is presented. The paper is organized as follows. In Sect.  2, we briefly present the main EAs, namely genetic algorithm , genetic programming , differential evolution , evolution strategies , and evolutionary programming . Section  3 describes the various uses of the considered methods in selected areas. Finally, recent advances and the current trends of the EAs are described.

2 Brief presentation of the EAs

2.1 genetic algorithms.

The genetic algorithm (GA) [ 1 ] is one of the oldest and most known optimization techniques, which are based on nature. In the GA, the search for solution space imitates the natural process which takes place in the environment, and the Darwinian theory of species evolution is taken into consideration. In GAs, we have a population of individuals; each, called a chromosome, represents a potential solution to the problem. The problem being solved is defined by the objective function. Depending on how “good” the given individual is fitted to the objective function, the value which represents its quality is attributed to it. This value is referred to as the fitness of the individual, and it is a main evaluating factor. Highly valued individuals have a better chance to be selected to the new generation of the population. In GAs, we have three operators: selection (a new population of individuals is created based on the fitness values of individuals from the previous generation), crossover (typically parts of individuals are exchanged between two individuals selected to the crossover), and mutation (the values of particular genes are changed randomly). Algorithm 1 presents the standard GA in the pseudo-code form (for more details see [ 7 ]).

Many modifications of the standard GA have been developed; some of them are listed in Table  1 .

2.2 Genetic programming

Genetic programming (GP) [ 2 ] is relatively new; it is a specialized form of a GA which operates on very specific types of solution, using modified genetic operators. The GP was developed by Koza [ 2 ] as an attempt to find the way for the automatic generation of the program codes when the evaluation criteria for their proper operation is known. Because the searched solution is a program, the evolved potential solutions are coded in the form of trees instead of linear chromosomes (of bits or numbers) widespread in GAs. As GP differs from GA the used coding schema, the main loop of GP is the same as in Algorithm 1. Of course, the genetic operators are specialized for working on trees, e.g., crossover as exchanging the subtrees, mutation as a change of node or leaf. Some modifications of the GP are shown in Table  1 .

2.3 Differential evolution algorithm

The differential evolution (DE) is a type of evolutionary algorithm useful mainly for the function optimization in continuous search space. Although a version of DE algorithm for combinatorial problems has also been discussed [ 51 ], the principal version of the DE algorithm was discussed by Storn and Price [ 3 ]. The main advantages of DE over a traditional GA are: It is easy to use, and it has efficient memory utilization, lower computational complexity (it scales better when handling large problems), and a lower computational effort (faster convergence) [ 52 ]. The standard DE procedure is shown in Algorithm 2. Presented there DE optimizes the problem with n decision variables. Parameter F scales the values added to the particular decision variables (mutation), and CR parameter represents the crossover rate [ 52 ] ( \(x_{i,j}\) is the value of j th decision variable stored in i th individual in the population). More detailed information on how the parameters should be tuned can be found in [ 53 ]. The main idea of the DE algorithm is connected with computing the difference between two individuals chosen randomly from the population. (The DE determines the function gradient within a given area—not at a single point.) Therefore, the DE algorithm prevents the solution of sticking at a local extreme of the optimized function [ 52 ]. Twenty years of DE development resulted in many modifications. Some of them are shortly presented in Table  1 .

2.4 Evolution strategies

The evolution strategies (ESs) are different when compared to the GAs, mainly in the selection procedure. In the GA, the next generation is created from the parental population by choosing individuals depending on their fitness value, keeping a constant size of the population. In the ES, a temporary population is created; it has the different size than the parental population (depending on the assumed parameters \(\lambda \) and \(\mu \) ). In this step, the fitness values are not important. Individuals in the temporary population undergo crossover and mutations. From such populations, an assumed number of the best individuals are selected to the next generation of the population (in a deterministic way). ESs operate on the vectors of the floating point numbers, while the classical GA operates on binary vectors. The primary types of ESs are ES( \(1+1\) ), ES( \(\mu +\lambda )\) , and ES( \(\mu ,\lambda \) ) [ 7 ].

2.4.1 Evolution strategy ES(1 + 1)

It is the oldest approach; only one individual x is evolved. The initial individual x is randomly generated. In each iteration, only one new individual y is created. The crossover operator does not exist, and the mutation operator creates the individual y by adding a randomly generated number to each gene of the individual x . The normal distribution N with a mean value equal to zero and a standard deviation equal to one is used. The value of i th gene in the individual y is computed as follows: \(y_{i}=x_{i}+\sigma \cdot {}N_{i}(0,1)\) , where \(\sigma \) is a parameter which determines the range of the mutation. Based on the fitness value of individuals x and y , the better one is selected for the new generation and becomes a new individual x . Parameter \(\sigma \) undergoes adaptation by the so-called rule of 1/5 successes. According to this rule, the best results are obtained when the relation R between successful mutations and all mutations is equal to 1/5. When during k successive generations, the relation R is higher than 1/5, then the value of the \(\sigma \) parameter is increased. When the relation R is lower than 1/5, then the value of the \(\sigma \) parameter is decreased. The \(\sigma \) parameter does not change when the relation R is equal to 1/5 [ 7 ].

2.4.2 Evolution strategy ES( \(\mu +\lambda \) )

This is an extension of the ES(1 + 1). The ES( \(\mu +\lambda \) ) has a self-adaptive mutation range, which replaces the 1/5 success rule implemented in ES(1 + 1). In the ES( \(\mu +\lambda \) ), each individual in the population contains additional chromosome \(\sigma \) , consisting of values of standard deviation for each gene. These values are used during mutation procedure. The crossover operator operates before the mutation. Both chromosomes (consisting of the value of variables, and of the value of \(\sigma \) parameters) undergo mutation and crossover processes [ 7 ]. Algorithm 3 presents the pseudo-code of the ES( \(\mu +\lambda \) ).

2.4.3 ES( \(\mu ,\lambda \) ) evolution strategy

This type of the ES is used more often than ES( \(\mu +\lambda \) ). The operation of both algorithms is almost identical. The only one difference is that in the ES( \(\mu ,\lambda \) ), the new population P ( t ) is created using only the best individuals from the “children” population M ( t ). In this case, \(\mu \) has to be greater than \(\lambda \) . Such selection gives the advantage of ES( \(\mu ,\lambda \) ) over the ES( \(\mu +\lambda \) ); in the latter, the population can be dominated by one individual which is much better than others and the values of standard deviations \(\sigma \) are not well tuned. The ES( \(\mu ,\lambda \) ) does not have this disadvantage because the individuals from the parental population \(P(t-1)\) are not copied to the new generation P ( t ) [ 7 ].

The pseudo-code of the ES( \(\mu ,\lambda \) ) is almost the same as Algorithm 3. The only one difference is line 10; here, it is: “10: select \(\mu \) the best individuals to population P ( t ) from the population M ( t ).”

Today, the covariance matrix adaptation evolution strategy (CMA-ES) is perceived as a state-of-the-art ES [ 54 , 55 ]. Several variants of CMA-ES were developed [ 55 ] to enhance the efficiency or robustness of the method by different techniques. In the CMA-ES algorithm, the adaptation of the population size or other parameters was presented in papers [ 56 ]. The CMA-ES algorithm employs global weighted recombination for both, strategy and object variables, adapts the full covariance matrix for mutation and, in general, is based on the scheme of the ES( \(\mu ,\lambda \) ). The CMA-ES algorithm can handle poorly scaled functions, and its performance remains invariant under rotation of the search space [ 54 ]. Some modifications of ESs are mentioned in Table  2 .

2.5 Evolutionary programming

Evolutionary programming (EP) was developed as a tool for discovering the grammar of the unknown language. However, EP became more popular when it was proposed as the numerical optimization technique. The EP is similar to the ES( \(\mu +\lambda \) ), but with one essential difference [ 7 ]. In EP, the new population of individuals is created by mutating every individual from the parental population, while in the ES( \(\mu +\lambda \) ), every individual has the same probability to be selected to the temporary population on which the genetic operations are performed. In the EP, the mutation is based on the random perturbation of the values of the particular genes of the mutated individual. The newly created and the parental populations are the same sizes ( \(\mu =\lambda \) ). Finally, the new generation of the population is created using the ranking selection of the individuals from both, the parental and the mutated populations. The pseudo-code of the standard EP method is presented in Algorithm 4. EP, like other evolutionary methods, has many modifications. Some of them are listed in Table  2 .

2.6 Evolutionary algorithms: problems and challenges

EAs are a very interesting research area. There are many open research problems such as: control of the balance between the exploration and exploitation properties; the self-adaptive (or adaptive) control of steering parameters; reducing the number of CMA-ES algorithm parameters; introducing new selection schemes; and increasing their effectiveness. The latter is important especially in the area of evolutionary design and in evolvable hardware. Also, new more efficient techniques for constraint handling are needed.

Additionally, more investigation into the application of EAs to dynamic optimization problems, to the optimization in noisy and non-stationary environments, and to multi-objective optimization problems (especially with a large number of decision variables) is required. Also, further research is needed in the population size adaptation in different optimization scenarios. Novel strategies should be developed to deal with expensive problems more competitively. Among these matters, there is the open question of constraint handling in EAs specifically to solve engineering optimization problems. As we know, the constraint handling methods can be classified into six main categories: penalty methods, methods evolving in the feasible region, methods using parallel population approaches, methods based on the assumption of superiority of feasible individuals, methods using multi-objective optimization techniques, and hybrid methods. Of course, each of these categories can be divided into several subcategories. The taxonomy of the constraint handling techniques with EAs can be found in the paper [ 77 ] by Petrowski et al. If we want to use the proper constraint handling method in EAs for real-world application, we should find the answer to the several questions such as is the objective function defined in the unfeasible domain (if not, the penalization methods cannot be used, for example): are there any active constraints at the optimum? (if not, the methods based on the search on the feasible region boundaries are irrelevant); what is the nature of the constraints? (if only one of the constraints is a nonlinear inequality, the methods for linear constraints are excluded). Moreover, in the real-world application of EAs with constraint handling techniques the effectiveness of a method is often dominated by two other decision criteria such as complexity and difficulty of implementation. Currently, penalty methods, feasibility rules, and stochastic ranking methods are used in real-world applications very often due to their simplicity [ 77 ]. Therefore, as we can see, there is no general approach for handling the constraints with EAs able to deal with any real-world problem, so the research on constraint handling techniques in EAs for real-world application is still a hot topic.

In EAs, there are many open research problems, which are discussed in more detail in [ 53 , 78 ].

Despite these weaknesses, we observe the growing popularity of EAs (please see Figs.  2 , 3 ). If we analyze the number of publications in the Web of Science (WoS) database (years 2000–2018) for particular EAs, we can see that their number is growing from year to year for the algorithms: GA, GP, DE, and ES. Only for EP algorithm, the number of published articles has been decreasing since the 2013 year. The total number of papers published in WoS database (years 2000–2018) which are related to these algorithms was equal to 98,596 for GA, 7038 for GP, 13,308 for DE, 1804 for ES, and 1585 for EP. Also, we have study the popularity of some EA methods in the selected scientific databases such as Google Scholar, Springer, IEEE Xplore, ACM, Scientific, Science Direct, Sage, Taylor, and Web of Science. The total number of the papers was equal to 1,304,205 for GA, 186,791 for GP, 119,668 for DE, 53,254 for ES, and 73,716 for EP.

Number of publications in the WoS database (years 2000–2018): GA ( a ), GP ( b ), DE ( c )

Number of publications in the WoS database (years 2000–2018): ES ( a ), EP ( b ), sum of publications for all listed algorithms GA, GP, DE, ES, EP ( c )

Also, many practical applications of EAs methods have been patented by such corporations like Caterpillar Inc., Yamaha Motor Co. Ltd., Fujitsu Limited, International Business Machines Corporation, Lsi Logic Corporation, Honda Research Institute Europe Gmbh, Prometheus Laboratories Inc., Siemens. The total number of patents registered in the Google Patents database (in years 2000–2018) for the particular EA methods was equal to 43,284 for GA, 2960 for GP, 2039 for DE, 1191 for ES, and 1583 for EP. More detailed information is presented in Table 3 .

We believe that over the next few years researchers will focus on the above areas.

3 Evolutionary algorithms in real-life problems

Similar to swarm intelligence algorithms [ 6 ], a major reason is a growing demand for smart optimization methods in many business and engineering activities. EAs are suitable mainly for optimization, scheduling, planning, design, and management problems. These kinds of problems are everywhere, in investments, production, distribution, and so forth. If we analyze, the results obtained from the WoS database (popularity of only ten first WoS categories for each method—for more detailed information see Table 4 ), we can see that the EAs methods are mainly used in the area such as:

Engineering electrical electronics,

Computer science artificial intelligence,

Computer science theory methods,

Computer science interdisciplinary applications,

Automation control system,

Computer science information systems,

Operations research management science.

When in WoS we will select a field Highly Cited in Field , we can see that the highly cited papers (in which EA methods are used) are from the following industry areas for the particular EA methods:

GA—energy fuels (EF), engineering electrical electronic (EEE), operations research management science (ORMS), engineering civil (EC),

GP—engineering civil (EC), water resources (WR), energy fuels (EF), automation control systems (ACS),

DE—energy fuels (EF), automation control systems (ACS), engineering electrical electronics (EEE), engineering civil (EC),

ES—construction building technology (CBT), energy fuels (EF), engineering civil (EC), engineering electrical electronic (EEE),

EP—construction building technology (CBT), engineering civil (EC), computer science software engineering (CSSE), transportation science technology (TST).

Therefore, in this paper, we will concentrate only on the real-world applications of particular EA methods in above areas of industry. In the next subsections, the abbreviation of industry area will be given in parenthesis after reference number to currently discussed paper.

3.1 Genetic algorithms in real-life problems

The GAs are a universal optimization tool. Using GAs, we can solve constrained optimization problems, multimodal optimization problems, continuous optimization problems, combinatorial optimization problems, and multi-objective optimization problems. Thus, there is a wide range of real-world applications of GAs. In this short section, we show only a few of them.

The paper [ 79 ] (EF) by Lv et al. presents the solar array layout optimization problem which is solved by GA. The presented numerical method is based on rotating model of a stratospheric airship to optimize the solar array layout. The results demonstrate that the proposed method is helpful in the preparation stage for installing large area flexible solar arrays. Also, it is shown that due to solar array optimization the output power of solar panel is significantly improved.

In paper [ 80 ] (EF) by Ma et al., the optimization model based on the GA, developed to reduce the energy consumption of high-sulfur natural gas purification process, is presented. A case study was performed in a high-sulfur natural gas purification plant with the capacity of \(300\cdot {}10^{4}\,\mathrm{N}\,\mathrm{m}^{3}/\mathrm{d}\) . The results demonstrate that the energy consumption of the purification plant was reduced by 12.7%.

In [ 81 ] (EEE), Yin et al. report a GA-inspired strategy designed and incorporated in the sequential evolutionary filter. Due to this strategy, the resampling used in most of existing particle filters is not necessary, and the particle diversity can be maintained. The experimental results show that the proposed sequential evolutionary filter offers better state estimation results than three other comparative filters.

The authors of [ 82 ] (EEE) investigate the pros and cons of hybridization of a GA and local search on the basis of a hard practical and up-to-date problem, namely the routing and spectrum allocation of multi-cast flows (RSA/M) in elastic optical networks (EONs). They proposed an efficient optimization method for solving the RSA/M problem in EONs. The proposed method outperformed all other competing methods. Additionally, introduction of Baldwin effects helped to preserve the population diversity in GA.

In the paper [ 83 ] (ORMS), the local-inventory-routing model for perishable products is presented. The proposed model integrates the three levels of a decision in the supply chain such as the number and location of required warehouses, the inventory level at each retailer, and the routes traveled by each vehicle. It is shown that the model developed in this paper is NP-hard; therefore, the authors develop a GA-based approach to solve this problem efficiently. It is shown that presented approach achieves a high-quality near-optimal solution in reasonable time.

The paper [ 84 ] (ORMS) by Ramos et al. presents new container loading algorithm with load balance, weight limit, and stability constraints which use a load distribution diagrams. This algorithm is based on multi-population biased random-key GA, with a new fitness function that takes static and loads balance into account. Due to incorporate weight balance goal with stability guaranteed by full base support and by the mechanical equilibrium conditions, the proposed approach is very effective.

In [ 85 ] (EC) by Yan et al., a framework to determine the investment plan to strengthen a railway system to earthquake hazard is proposed. This framework consists of four parts. In the third part, an investment optimization model is formulated, and in part four, this model is solved using GA. The proposed approach has been applied to the real Chinese railway system. The obtained results show that the presented framework is more responsive to the earthquake impact on railway system compared to topology-based methods.

In the paper [ 86 ] (EC) by Ascione et al., the multi-objective optimization of operating cost for space conditioning and thermal comfort to achieve a high level of building energy performance is presented. The main objective of proposed GA is to optimize the hourly set point temperatures with a day-ahead horizon, based on a forecast of weather conditions and occupancy profile. In comparison with the standard control strategy, the presented approach generates a reduction of operating cost up to 56%.

In [ 87 ] (EC) by Lin et al., a time-optimal train running reference curve is designed with least time-consuming, but highest energy consumption, and it is optimized by adding multi-point coasting control to realize energy saving with a relative rise in time. Multi-population GA is adopted to solve this multi-point combinatorial optimization problem. Simulation results, based on real line condition and train parameters of Shanghai line 7, demonstrate the advancement of multi-point coasting control with the proposed approach.

In [ 88 ] (EC), the application of GA to minimization of average delay for an urban signalized intersection under the oversaturated condition is presented. Relieving urban traffic congestion is an urgent call for traffic engineering. One of the key solutions to reduce congestion is the effectiveness of traffic signalization. The current traffic signal control system is not fully optimized for handling the oversaturated condition. Simulation results show that GA is able to control the traffic signals for minimizing the average delay to 55 s/vehicle.

Zhang et al. [ 89 ] (EEE) use the flexible GA for node placement problems. Node placement problems are encountered in various engineering fields, e.g., the deployment of radio-frequency identification systems or wireless sensor networks. The flexible GA with variable length encoding, subarea-swap crossover, and Gaussian mutation is able to adjust the number of nodes and their corresponding properties automatically. Experimental results show that the flexible GA offers higher performance than existing tools for solving node placement problems.

In the paper [ 90 ] (EF) by Reddy, the scheduling problem considering the hybrid generation system is presented. The new strategy based on GA for the optimal scheduling problem taking into account the impact of uncertainties in the wind, solar photovoltaic modules with batteries, and load demand forecast is proposed. From simulation results (for IEEE 30 and 300 bus test systems), it can be noticed that with a marginal increase in the cost of day-ahead generation schedule, a significant reduction in real-time mean adjustment cost is obtained.

3.2 Genetic programming in real-life problems

The GP possesses many practical applications.

In [ 91 ] (ACS) et al. the handwriting character recognition system for inertial-sensor-equipped pens is presented. In this system, the characteristic function is calculated for each character using a GP algorithm. The experimental results show that the performance of the proposed method is superior to that one of the state-of-the-art works in the area of recognizing Persian/Arabic handwriting characters.

Bagatur and Onen [ 92 ] (WR) propose novel models for the prediction of flood routing in natural channels using the gene expression programming (GEP) algorithm, which is one of the extensions of GP algorithm. The GEP method makes use of few hydrologic parameters such as inflow, outflow, and time. The performance of the proposed models is evaluated by two goodness-of-fit measures. The proposed GEP models are tested for the three datasets taken from the literature. It is proved that the GEP models show superior performance to the other solution techniques based on the Muskingum model.

In the paper [ 93 ] (EF) by Abkenar et al., an intelligent fuel cell (FC) power management strategy is proposed. The main objective of the proposed approach is to improve FC performance at different operating points without employing DC/DC interfacing converters. A hybrid all-electric ships (AES) driveline model using GP is utilized to formulate operating FC voltage based on the load current, FC air, and fuel flow rates. The proposed approach maintains FC performance and reduces fuel consumption, and therefore ensures the optimal power sharing between the FC and the lithium-ion battery in AES application.

The authors of [ 94 ] (EC) use GP algorithm to develop models to predict the deterioration of pavement distress of the urban road network. Five models for the prediction of pavement distress progression such as cracking, raveling, pothole, rutting, and roughness are created. In order to obtain a training dataset, and validation dataset, the real data from the roads of Patiala City, Punjab, India, have been collected. It was shown that GP models predict with high accuracy for pavement distress and help the decision makers for adequate and timely fund allocations for the preservation of the urban road network.

3.3 Differential evolution in real-life problems

The DE algorithm also found many real-world applications.

In [ 95 ] (EF), Ramli et al. present an application of multi-objective SaDE algorithm for optimal sizing of a photovoltaic (PV)/wind/diesel hybrid microgrid system (HMS) with battery storage. The multi-objective optimization is used to analyze the loss of power supply probability, the cost of electricity, and the renewable factor in relation to HMS cost and reliability. The proposed approach is tested using three case studies involving differing house numbers for the city Yanbu, Saudi Arabia. The results obtained are useful in investigating optimal scheduling of HMS components and can be used as a power reference for the economic operation of PV and wind turbine generators.

Yao et al. [ 96 ] (EF) use a multi-objective DE algorithm for optimizing a novel combined cooling, heating, and power-based compressed air energy storage system. The system combines a gas engine, ammonia–water absorption refrigeration system, and supplemental heat exchangers. The proposed optimization technique is used to find a trade-off between the overall exergy efficiency and the total specific cost of final product. The best trade-off solution which was selected possesses a total product unit cost of 20.54 cent/kWh and an overall exergy efficiency of 53.04%.

In the paper [ 97 ] (ACS) by Wang et al., the DE algorithm is applied for wind farm layout optimization with the aim of maximizing the power output. Due to a new encoding mechanism in DE, the dimension of the search space is reduced to two, and a crucial parameter (i.e., the population size) is eliminated. In comparison with seven other methods, the proposed approach is able to obtain the best overall performance, in terms of the power output and execution time.

The authors of [ 98 ] (EEE) investigate the problem of linear dipole array synthesis. Dynamic DE algorithm is proposed for synthesizing shaped power pattern by using element rotation and phase optimization for a linear dipole array. Based on two experiments for synthesizing flattop and cosecant squared pattern, the effectiveness and advantages of the proposed approach were verified in comparison with the phase-only optimization and the amplitude-phase joint optimization.

Tian et al. [ 99 ] (EC) use a multi-objective hybrid DE+PSO algorithm in order to create a set of Pareto solutions for the problem of dual-objective scheduling of rescue vehicles to distinguish forest fires. The novel multi-objective scheduling model to handle forest fires subject to limited rescue vehicles constraints, in which a fire spread model is introduced into this problem to better describe practical forestry fire is presented. Results show that the proposed approach is able to quickly produce satisfactory Pareto solutions in comparison with GA and PSO algorithms.

3.4 Evolution strategies in real-life problems

Studying the literature, we can find fewer papers with the real-life applications of ESs than those with GAs. Below we shortly present some of them.

The paper [ 100 ] (CBT) by Hasancebi presents ES integrated parallel optimization algorithm to minimize the total member weight in each test steel frame. Steel frames with various beam–column connection and bracing configuration are considered for comparative cost analyzes. Three multi-story buildings are chosen (10, 20, and 30-story buildings) as examples for numerical verification of proposed method. The results collected are utilized to reach certain recommendations regarding the selection of economically feasible frames for the design of multi-story steel buildings.

In [ 101 ] (EF), Fadda et al. consider the usage of electric batteries in order to mitigate it. In energy distribution systems, uncertainty is the single major cause of power outages; therefore, the authors propose intelligent battery able to maximize its lifetime while guaranteeing to satisfy all the electric demand peaks. The battery exploits a customized steady-state ES to dynamically adapt its recharge strategy to changing environments. Experimental results on both synthetic and real data demonstrate the efficacy of the proposed approach.

In the paper [ 102 ] (EC) by Ogidan et al., the enhanced non-dominated sorting ES algorithm that uses a specialized operator to guide the algorithm toward known sanitary sewer overflows (SSOs) locations is presented. The main objectives of the proposed method are the maximization of SSO reduction and minimization of rehabilitation cost. The proposed method was tested in an existing network in the eastern San Antonio Water System network. The presented approach improves the convergence rate by approximately 70% over the tested alternative algorithms.

The authors of [ 103 ] (EEE) investigate the problem of wireless sensor fault diagnosis based on fusion data analysis. The fault diagnosis model is proposed based on the hierarchical belief rule-based model, and the CMA-ES algorithm is used to optimize the initial parameters of the proposed model. In order to validation of presented approach, a case study based on Intel laboratory dataset of sensors is designed. The experiments prove the effectiveness of the proposed method in comparison with back propagation neural network model and the fuzzy expert system.

In the paper [ 104 ] (EEE), the problem of subsurface inverse profiling of a 2-D inhomogeneous buried dielectric target is presented and solved using proposed iterative optimization method which is based on CMA-ES algorithm. In relation to the results obtained using EP and PSO, the results obtained using CMA-ES significantly outperform the other two optimization techniques in the inhomogeneous imagining.

The paper [ 105 ] (EEE) by Emadi et al. presents CMA-ES algorithm for tasks scheduling in the cloud computing environment. The need for planning the scheduling of the user’s jobs is an important challenge in the field of cloud computing. The causes are manifold; the most important are: ever-increasing advancements of information technology, an increase in applications and user needs for these applications with high quality, also the popularity of cloud computing among user, and rapid growth of them during recent years. The results obtained indicate that presented algorithm, led to a reduction in execution time of all tasks, compared to the shortest processing time algorithm, longest processing time algorithm, and GA and PSO algorithms.

3.5 Evolutionary programming in real-life problems

In the literature, we can find the applications of EP in many different areas. However, in WoS the number of papers in which EP algorithm is used is decreasing since the 2013 year. Below we shortly present some of them.

The paper [ 106 ] (TST) by Yan et al. presents bi-subgroup self-adaptive EP algorithm for seeking the Pareto optimal solution of the multi-objective function of the hybrid electric vehicle (HEV) and the best degree of hybrid (DOH) for this vehicle. In the proposed algorithm, the evolution of Cauchy operator and Gauss operator are parallel performed with different mutation strategies. Moreover, the Gauss operator owns the ability of self-adaptation according to the variation of adaptability function. The simulation results show that the optimal DOH is equal to 0.311 for given HEV. Also, the validity of simulating method was proved, and the fuel saving effect was consistent with authors’ expectations.

In the paper [ 107 ] (CBT) by Gao, a new evolutionary neural network whose architecture and connection weights simultaneously evolve is proposed. This neural network is based on immunized EP algorithm and is used in the novel inverse back analysis for underground engineering. As a numerical example, an underground roadway of the Huainan coal mine in China is chosen for the verification of the accuracy of the presented inverse back analysis. The results obtained show that using the proposed method, the computed displacements agree with the measured ones. Therefore, it is demonstrated that the new inverse back analysis method is a high-performance method for usage in underground engineering.

Jiang et al. [ 108 ] (EC) use EP algorithm to find weights and the threshold value in the neural network which is applied to the traffic signal light control. According to the historical traffic flow data of a crossroad, the next node’s traffic flow data are predicted. Due to predicted data, the traffic signal light frequency can be re-adjusted in order to improve traffic congestion and other traffic problems. The results obtained show that the connection of EP algorithm with the neural network has a good effect on traffic signal light optimization.

The authors of [ 109 ] (CSSE) propose a novel approach to navigate over 3-D terrain using best viewpoints. The concept of viewpoint entropy is exploited for best view determination, and greedy n -best view selection is used for visibility calculation. In order to connect the calculated viewpoints, the authors use an EP algorithm for the traveling salesman problem. It was shown that the computed and planned viewpoints reduce human effort when used as starting points for scene tour. The proposed method was tested on real terrain and road network datasets.

3.6 Which EA should be used for a given problem?

What lesson for a potential user of evolutionary computation emerges from the above overview? The question is simple, but the answer is hard. All discussed methods are from the same family—evolutionary approaches to optimization problems. The principal question could be: Which of the discussed methods is suitable for the given problem? Expanding the answer to all heuristic methods in general, not just evolutionary algorithms, the best answer seems to be: take the method you know best, you can define your problem well in terms required by this method, you understand the sensitivity of this method to parameters, you can fine-tune this method. Let us see, for example, on energy fuels area. Numerous evolutionary approaches are applied within this scope. It is not possible to indicate one of them as the best for this particular subject. The similar situation is with other areas of industry.

As we can see, the literature on the evolutionary algorithms in general and in their industrial applications is plentiful, but very rarely this literature concerns applications that have been used in practice. Following [ 110 ], we can say that the theory does not support the practice; there is a big gap between theory and practice. Theoretical results on properties such as convergence, diversity, exploration, exploitation, deceptiveness, and epistasis are not useful enough for practice. Significant topics from the practice point of view are constraint and noise handling methods, robustness, or multi-objective optimization. The progress in the above matters is also observed; however, these methods are tested mainly on simple silo problems or standard sets of numerical functions, so their usefulness to practitioners working on EA-based software applications is very limited.

It is worth mentioning that the real usefulness of EAs could be not only in industry. The spectacular achievement of EA is presented in [ 111 ]. The artificial intelligence system, with the use of EA, the first time discovered a new theory, namely a mechanism of planar regeneration. The remarkable ability of these small worms to regenerate body parts made them a research model in human regenerative medicine.

4 Summary and future trends

As it is shown in this paper, the evolutionary algorithms are a popular research domain. Each year many new modifications of these algorithms are proposed. Some of these modifications are shortly described in Tables  1 and 2 . The EAs are applied to solve many industry problems. When we cannot use a dedicated algorithm for a given problem, one of the EAs will be a good choice. Of course, we must remember about specific issues the user can face when dealing with EAs. Here, we can mention two main problems. First of this problem is a premature convergence (the population converging to a suboptimal solution instead of an optimal one). We can solve this problem by introducing the mechanism which will provide a lower transfer rate of the genetic material between individuals—the whole population is divided into several subpopulations (so-called islands) and periodically migrate an individual between islands [ 15 ]. Another solution of the premature convergence problem is a cooperation of EAs with branch and bound algorithm endowed with interval propagation techniques, as it was shown in [ 112 ]. The second problem is related to the optimal trade-off between exploration and exploitation properties of EA. One of the solutions to this problem is control of the level of selection pressure [ 113 ]. We can do this by introducing specialized genetic operators which will guarantee high population diversity at the start of the algorithm operation (high exploration property–small exploitation property) and a low population diversity at the end of the algorithm operation (low exploration property–high exploitation property). A survey about exploration and exploitation in EAs can be found in [ 114 ].

As future trends in EAs, we can mention some main directions. The first of current trend is a hybridization of two or more algorithms to obtain better results. Currently, in the literature, we can find an increasing number of papers where hybrid algorithms are presented. Also, many researchers work on modifications of EAs to improve their computational performance. In many recently published papers, we can find modifications of GA [ 22 , 23 , 115 , 116 ], GP [ 31 , 32 , 117 , 118 ], DE [ 48 , 49 , 119 , 120 ], ES [ 64 , 65 ], and EP [ 74 , 75 ]. An interesting domain of future research in EAs is also memetic algorithms. The term memetic algorithm is widely used as a synergy of the evolutionary algorithm or any other population-based approach with separate local search techniques as the Nelder–Mead method. We can find very interesting information about future trends in EAs in the paper [ 121 ] written by Eiben et al. As one of the future trends in EAs, the authors point out the increasing interest in applying EAs to embodied or embedded systems, that is, employing evolution in populations for which the candidate solutions are controllers or drivers that implement the operational strategy for some situated entities, and are evaluated within the context of some rich, dynamic environment: not for what they are, but for what they do. Finally, there is another one important issue especially in the industrial application of EA methods. Very often in real-world problems, we must optimize a function in a high-dimensional domain. This process usually is very complex and takes a lot of computational time. Therefore, in real applications, the EAs designed for this type of problems should be designed to be implemented easily to run in parallel (or easy to run in GPU) to reduce their computational time. A greater effort in this feature should be in future proposals because this could be a crucial feature to decide whether an algorithm is useful in real applications. Some research in the area of EAs can be connected with the so-called surrogate models (computationally cheaper models of real-world problems) which can be used in the place of full fitness evaluation, and that refine those models through occasional full evaluations of individuals in the population [ 121 ]. Also, very often industry problems have many objectives. In tandem with algorithmic advances, the interactive evolutionary algorithms are used to increase the efficiency of EAs in multi-objective optimization [ 121 ]. As we know, each engineering problem is defined by the different objective function and has a different landscape of search space. The values of EAs parameters which are “good” in one problem cannot be sufficient in another one. Therefore, searching for new techniques in such area as automated tuning and adaptive parameter control is still a hot topic in EAs. Another important issue in the industrial application of EA methods is a proper definition of an objective function. The industrial problems are very complex. Therefore, a definition of a good mathematical model (good objective function for EAs) for a given industry process is also a very demanding task. The “quality” of the chosen objective function will have a great influence on the results obtained using EA methods. The next issue which we want to mention in discussing is repeatability of the EA methods. As we know, the EAs are stochastic techniques. Each time the EA method is run, a different result can be obtained. Therefore, the main focus should be on ensuring repeatability of the results generated by EA techniques. This issue is very important for application on EA methods in industry.

In summary, we believe that in the future, new evolutionary algorithms will be developed, and the research problems connected with evolutionary algorithms will always be a hot topic for researchers.

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Department of Electronics and Computer Science, Koszalin University of Technology, Sniadeckich 2 Street, 75-453, Koszalin, Poland

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Slowik, A., Kwasnicka, H. Evolutionary algorithms and their applications to engineering problems. Neural Comput & Applic 32 , 12363–12379 (2020). https://doi.org/10.1007/s00521-020-04832-8

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What is Genetic Programming (GP)?

How genetic programming works, sources of information about the field of genetic programming (gp), genetic algorithms (ga), and the field of genetic and evolutionary computation (gec), conferences about genetic programming (gp) and genetic and evolutionary computation (gec), application areas for genetic programming, news about genetic programming, parallelization of genetic programming, john koza’s publications on genetic programming.

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Genetic programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem. Genetic programming starts from a high-level statement of “what needs to be done” and automatically creates a computer program to solve the problem.

There are now 36 instances where genetic programming has automatically produced a result that is competitive with human performance , including   15 instances where genetic programming has created an entity that either infringes or duplicates the functionality of a previously patented 20 th -century invention, 6 instances where genetic programming has done the same with respect to a 21 st -centry invention, and 2 instances where genetic programming has created a patentable new invention.

Given these results, we say that “Genetic programming now routinely delivers high-return human-competitive machine intelligence.” Click here for our definitions of “human-competitive,”   the “AI ratio” (“artificial-to-intelligence” ratio) and “high-return,”   “routine,” and “machine intelligence.” This statement is the most important point of the 2003 book Genetic Programming IV : Routine Human-Competitive Machine Intelligence . Click here to read chapter 1 of Genetic Programming IV in PDF format.   Click here for 2004 awards for human-competitive results (based on presentations at the GECCO-2004 conference in Seattle on June 27, 2004).

The fact that genetic programming can evolve entities that are competitive with human-produced results suggests that genetic programming can be used as an automated invention machine to create new and useful patentable inventions. In acting as an invention machine, evolutionary methods, such as genetic programming, have the advantage of not being encumbered by preconceptions that limit human problem-solving to well- troden paths. Genetic programming has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time (over the 15-year period from 1987 to 2002).

Genetic programming has 16 important attributes that one would reasonably expect of a system for automatic programming (sometimes also called program synthesis or program induction ). Genetic programming has seven important differences from conventional approaches to artificial intelligence (AI) and machine learning (ML). For additional information, click here for PowerPoint (PPT) presentation on genetic programming (about 5 Megabytes) similar to that presented at the 2003 Accelerating Change Conference on September 13, 2003 and similar to the overview lecture given on September 24, 2003 in John Koza’s course at Stanford University on genetic algorithms (GA) and genetic programming (GP).

Genetic programming starts with a primordial ooze of thousands of randomly created computer programs. This population of programs is progre ss ively evolved over a series of generations. The evolutionary search uses the Darwinian principle of natural selection (survival of the fittest) and analogs of various naturally occurring operations, including cro ss over (sexual recombination), mutation, gene duplication, gene deletion. Genetic programming sometimes also employs developmental proce ss es by which an embryo grows into fully developed organism. Old Chinese saying says “animated gif is worth one mega-word,” so click here for short tutorial of “What is GP?” including about two dozen animated gifs . This short tutorial contains a discu ss ion of the preparatory steps of a run of genetic programming, the executional steps (that is, the flowchart of genetic programming ), an illustrative simple run of genetic programming for a problem of symbolic regre ss ion of a quadratic polynomial, a discu ss ion of developmental genetic programming for the automatic synthesis of both the topology and sizing of analog electrical circuits (potentially also including placement and routing), and the use of a turtle to draw complex structures (such as antenna). In addition, genetic programming can automatically create, in a single run, a general (parameterized) solution to a problem in the form of a graphical structure whose nodes or edges represent components and where the parameter values of the components are specified by mathematical expre ss ions containing free variables. That is, genetic programming can automatically create a general solution to a problem in the form of a parameterized topology .

The technique of genetic programming (GP) is one of the techniques of the field of genetic and evolutionary computation (GEC) which, in turn, includes techniques such as genetic algorithms (GA), evolution strategies (ES), evolutionary programming (EP), grammatical evolution (GE), and machine code (linear genome) genetic programming.

• 16 authored books, 4 videos, and 4 edited books on genetic programming (GP) , including 6 books in the Genetic Programming Book series from Kluwer Academic Publishers (as part of the bigger list of 73 authored books, 32 edited books, and 4 videos on genetic and evolutionary computation ).
• 17 conference proceedings books on genetic programming (GP) , including the 3 annual GP conferences, 5 annual GECCO conferences (that now include the annual GP conference), 6 annual Euro-GP conferences, the 2003 Genetic Programming Theory and Practice workshop (GPTA), the 1995 AAAI Symposium on Genetic Programming, and the 1995 Workshop on Genetic Programming (as part of a bigger list of 99 conference proceedings books on evolutionary computation) .
• 3,440 published papers on genetic programming (as of November 28, 2003) in a searchable bibliography (with many on-line versions of papers) by over 880 authors maintained by William Langdon’s and Steven M. Gustafson
• Over 4,000 published papers on evolutionary computation in a searchable bibliography maintained by Karsten Weicker and Nicole Weicker containing entries on genetic and evolutionary computation and related areas (e.g. artificial life).
• About two dozen conferences with Published Proceedings that are held regularly in the field of genetic programming and genetic and evolutionary computation (GEC)
• 2004 awards for human-competitive results (based on presentations at the GECCO-2004 conference in Seattle on June 27, 2004).
• E-Mail Mailing List on Genetic Programming , the EC-Digest (formerly the GA-Digest), and other mailing lists.
• Genetic Programming and Evolvable Machines journal (published by Kluwer Academic Publishers and edited by Wolfgang Banzhaf) (started January 2000). This journal is available as part of membership in the International Society for Genetic and Evolutionary Computation (ISGEC)
• Evolutionary Computation journal (published by The MIT Press and edited by Marc Schoenauer). This journal is available as part of membership in International Society for Genetic and Evolutionary Computation (ISGEC)
• IEEE Transactions on Evolutionary Computation journal (published by IEEE Neural Network Society and edited by Xin Yao)
• Software for genetic programming, genetic algorithms, and other evolutionary computation techniques, including the "Little LISP" Computer Code for Genetic Programming as Contained in 1992 book Genetic Programming (Koza 1992)
• 37 completed Ph.D. theses on genetic programming
• 58 students working on thesis involving genetic programming
• A partial list of people compiled by Bill Langdon who are active in genetic programming
• International Society for Genetic and Evolutionary Computation (ISGEC) . ISGEC is the only membership organization in the field of genetic and evolutionary computation. It operates of the annual GECCO conference (largest conference in the field of genetic and evolutionary computation) and the biannual FOGA conference.
• Evo-Net —The Network of Excellence in Evolutionary Computation (an extensive clearinghouse of information about the field of genetic and evolutionary computation and operator of the annual Euro-GP conferences and the Evo-Net workshops)
• The GA Archives , including back i ss ues of the GA-Digest and EC-Digest, genetic algorithm code in various programming languages, an extensive list of conference announcements in the field of genetic and evolutionary computation, etc.
• Book series of genetic programming for Kluwer Academic Publishers book series on genetic programming, edited by John R. Koza
• Book series on genetic algorithms and evolutionary computation from Kluwer Academic Publishers, edited by David E. Goldberg.
• Courses (and short courses) at various universities on genetic algorithms, genetic programming, and evolutionary computation
• For information about John Koza’s course on genetic algorithms and genetic programming at Stanford University
• 11 Books of Student Papers from John Koza's Courses at Stanford University on genetic algorithms and genetic programming and artificial life
• 6 Course Readers from John Koza's courses at Stanford University on genetic algorithms and genetic programming and artificial life
• John Koza's home page at Stanford University
• For information about the 1992 book Genetic Programming: On the Programming of Computers by Means of Natural Selection , the 1994 book Genetic Programming II: Automatic Discovery of Reusable Programs , the 1999 book Genetic Programming III: Darwinian Invention and Problem Solving , and the 2003 book Genetic Programming IV : Routine Human-Competitive Machine Intelligence . Click here to read chapter 1 of Genetic Programming IV book (2003) in PDF format.
• 36 human-competitive results produced by genetic programming , including 21 previously patented inventions replicated by genetic programming and 2 patentable new inventions generated by genetic programming.
• Link to http://www.genetic-programming.COM (“genetic-programming.COM” WITH the hyphen) (Genetic Programming Inc.) including information about 1,000-Pentium parallel computer for doing genetic programming research.
• Jobs for scientific research programmer at Genetic Programming Inc.
• Link to Jaime Fernandez’s genetic programming notebook site (“geneticprogramming.com” WITHOUT the hyphen)
• David Beasley’s Frequently Asked Questions about genetic and evolutionary computation . This comes in 6 parts. Part 2 has a summary of the different types of genetic and evolutionary computation.
• Annual 2005 Genetic and Evolutionary Computation (GECCO) conference to be held on June 25–29, 2005 (Saturday – Wednesday) in Washington DC . GECCO is the largest conference in the field of genetic and evolutionary computation. The GECCO-2005 conference is a combination of the 10 th annual Genetic Programming Conference (GP-2005) and the 14 th International Conference on Genetic Algorithms (ICGA-2005). GECCO is operated by the International Society for Genetic and Evolutionary Computation ( ISGEC ).
• Annual 2005 Euro-Genetic-Programming Conference (and the co-located Evolutionary Combinatorial Optimization conference and other Evo-Net workshops) to be held on March 30 – April 1, 2005 (Wednesday-Friday) in Lausanne , Switzerland .
• Annual NASA/DoD Conference on Evolvable Hardware (EH) to be held on June 24 – 26 (Thursday – Saturday), 2004 in Seattle.
• Genetic Programming Theory and Practice (GPTP) workshops at the University of Michigan in Ann Arbor in 2003 and 2004 and 2005 Genetic Programming Theory and Practice (GPTP) workshop to be held at the University of Michigan in Ann Arbor
• 2004 Asia-Pacific Workshop on Genetic Programming (ASPGP) held in Cairns , Australia on December 6-7 (Monday-Tuesday), 2004
• Past GP conferences f or 1996, 1997, and 1998 (including the SGA-98, the Symposium on Genetic Algorithms)
• Past Euro-GP conferences for 1998, 1999, 2000, 2001, 2002, and 2003
• Past GECCO conferences (Genetic and Evolutionary Computation Conferences) for 1999, 2000, 2001, 2002, 2003, and 2004. Starting in 1999, the annual GECCO conference includes the annual Genetic Programming Conference.

There are numerous applications of genetic programming. We are particularly interested in applying genetic programming to

• “Black Art Problems,” such as the automated synthesis of analog electrical circuits, controllers, antennas, networks of chemical reactions, optical systems, and other areas of design,
• “Programming The Unprogrammable ” (PTU) involving the automatic creation of computer programs for unconventional computing devices such as cellular automata, multi-agent systems, parallel programming systems, field-programmable gate arrays, field-programmable analog arrays, ant colonies, swarm intelligence, distributed systems, and the like, and
• Commercially Useful New Inventions (CUNI) involving the use of genetic programming as an automated "invention machine" for creating commercially usable new inventions.

We are constantly looking for new domain areas in which to apply the techniques of genetic programming to achieve human-competitive machine intelligence.

· For May 2003 IEEE Intelligent Systems article “What’s AI done for me lately? Genetic programming’s human-competitive results”, visit IEEE Intelligent Systems . Click here for PDF file .

· For February 2003 Scientific American article “Evolving inventions” on genetic programming by John Koza, Martin A. Keane, and Matthew J. Streeter, visit Scientific American.

· For Salon article on "Software that Writes Software" b y Alexis Willihnganz (August 10, 1999)

· For E. E. Times article on automatic synthesis of analog electrical circuits using genetic programming .

· For article in Computerbits on genetic programming .

· For Scientific American article by W. Wayt Gibbs on genetic programming.

· For Busine ss Week article (June 23, 1997) entitled "Stanford Eggheads and Entrepreneurs"

· For Busine ss Week article (August 25, 1997) entitled "What Matters is How Smart You Are"

· For U. S. News and World Report article on evolutionary computation and genetic programming.

· For Slashdot.org posting (August 10, 1999).

· For the451.com article entitled "Re-inventing the 'invention machine" (April 14, 2000).

In July 1999, Genetic Programming Inc. started operating a new 1,000-node Beowulf-style parallel cluster computer consisting of 1,000 Pentium II 350 MHz proce ss ors and a host computer. Click here for technical discu ss ion of parallel genetic programming and building the 1,000-Pentium Beowulf-style parallel cluster computer . About half of the 36 human-competitive results produced by genetic programming were obtained using computing systems that were substantially smaller than the 1,000-Pentium computer mentioned above. Fifteen of these human-competitive results were obtained on a 1995-vintage parallel computer system composed of 64 PowerPC 80 MHz proce ss ors with a spec95fp rating. This 1995-vintage computer has total computational power equal to only about 1/60 of that of the 1000-Pentium machine mentioned above. Five of these results were obtained on a 70-Alpha machine (whose spec95fp rating is 1/9 of that of the 1,000-Pentium machine mentioned above). One of these human competitive results were obtained with a 1994-vintage machine (whose spec95fp rating is 1/1,320 of that of the 1,000-Pentium machine mentioned above). The individual proce ss ors in the1 ,000 -Pentium machine have (as of July 2003) about 1/8 the speed of proce ss ors contained in commercially available $999 laptops, so that the 1,000-Pentium machine is approximately equivalent to a 125-proce ss or machine with 2003-vintage proce ss ors.

1000-Pentium Beowulf-Style Cluster Computer

( left and right sides) (july 29, 1999).

For picture of uninterruptable power supply (UPS) for new 1000-Pentium computer. Design and contracting of site for 1000-Pentium computer by Gordon Prill Inc . of Mountain View , California . The 1,000-Pentium machine was a ss embled by Stan Fox of the COMPAQ Sunnyvale Staging Center . For picture of earlier 70-node parallel computer with Senator Barbara Boxer (California), John Koza (back row), Oscar Stiffelman (front row), Forrest H Bennett III, and William Mydlowec. For picture of earlier 70-node parallel computer with Ellen Goldberg (President of Santa Fe Institute) , John Koza, Forrest H Bennett III, and Oscar Stiffelman.

• 1992 book on genetic programming entitled Genetic Programming: On the Programming of Computers by Means of Natural Selection from The MIT Pre ss . The MIT Pre ss also publishes a videotape entitled Genetic Programming: The Movie a ss ociated with the first book. Click here for more information about this 1992 videotape .
• 1994 book on genetic programming entitled   Genetic Programming II: Automatic Discovery of Reusable Programs from The MIT Pre ss . The MIT Pre ss also publishes a videotape entitled Genetic Programming II Videotape: The Next Generation. associated with this second book. Click here for additional information about this 1994 videotape .
• 1999 book Genetic Programming III: Darwinian Invention and Problem Solving from Morgan Kaufmann (by John R. Koza, Forrest H Bennett III, David Andre, and Martin A. Keane). Morgan Kaufmann also publishes Genetic Programming III Videotape: Human-Competitive Machine Intelligence (by John R. Koza, Forrest H Bennett III, David Andre, Martin A. Keane, and Scott Brave). Click here for information about this 1999 videotape .
• 2003 book Genetic Programming IV : Routine Human-Competitive Machine Intelligence from Kluwer Academic Publishers (by John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, Je ss en Yu, and Guido Lanza ) (ISBN 1-4020-7446-8) Kluwer Academic Publisher also publishes a DVD disk Genetic Programming IV: Video: Routine Human-Competitive Machine Intelligence (by John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, Je ss en Yu, Guido Lanza, and David Fletcher) that is bound into this 2003 book.
• Stanford University technical reports from the Computer Science Department and Stanford BioMedical Informatics of which I am author or co-author can be obtained on the web, including
• STAN-TR-CS 1314 (1990) entitled Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems
• STAN-TR-CS 1528 (1994) entitled Architecture-Altering Operations for Evolving the Architecture of a Multi-Part Program in Genetic Programming
• STAN-TR-CS 1542 (1995) entitled Parallel Genetic Programming on a Network of Transputers
• SMI-95-0586 (1995) entitled A Programming Course in Bioinformatics for Computer and Information Science Students
• SMI-2000-0851 (2000) entitled Reverse Engineering and Automatic Synthesis of Metabolic Pathways from Observed Data Using Genetic Programming
• Abstracts, citations, and copies of research papers (almost all available in Post Script or PDF) by John Koza:

Click here for list of patents

Contact Information

Please send corrections or additions to this page to:

John R. Koza

Genetic Programming Inc. (Third Millennium On-Line Products Inc.)

Post Office Box K

Los Altos , California 94023 USA

FAX: 650-941-9430

E-mail: [email protected]

E-mail: [email protected]

· For information about t he annual Genetic and Evolutionary Computation Conference (GECCO) operated by the Association for Computing Special Interest Group on Genetic and Evolutionary Computation (SIGEVO)

· For information about the annual Human-Competitive Awards (the “ humies ”) in genetic and evolutionary computation offered at the annual Genetic and Evolutionary Computation Conference (GECCO)

· The home page of Genetic Programming Inc. at www.genetic-programming.com .

· The home page of John R. Koza (including online versions of most published papers)

· For information about John Koza’s course on genetic algorithms and genetic programming at Stanford University

· For information about National Popular Vote

· Information about the 1992 book Genetic Programming: On the Programming of Computers by Means of Natural Selection , the 1994 book Genetic Programming II: Automatic Discovery of Reusable Programs , the 1999 book Genetic Programming III: Darwinian Invention and Problem Solving , and the 2003 book Genetic Programming IV : Routine Human-Competitive Machine Intelligence . Click here to read chapter 1 of Genetic Programming IV book in PDF format.

· 4,000+ published papers on genetic programming (as of November 28, 2003) in a searchable bibliography (with many on-line versions of papers) by over 880 authors maintained by William Langdon’s and Steven M. Gustafson.

· For information on the Genetic Programming and Evolvable Machines journal

· For information on the Genetic Programming book series, see the Call For Book Proposals

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Title: sharpness-aware minimization in genetic programming.

Abstract: Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) function and the so-called fitness sharpness. The latter serves as a %connection between the geometry of the fitness landscape measure of the nonlinear behavior of a solution %and generalization and does so by finding solutions that lie in neighborhoods having uniformly similar loss values across all fitness cases. In this contribution, we adapt SAM for tree Genetic Programming (TGP) by exploring the semantic neighborhoods of solutions using two simple approaches By capitalizing upon perturbing input and output of program trees, sharpness can be estimated and used as a second optimization criterion during the evolution. To better understand the impact of this variant of SAM on TGP, we collect numerous indicators of the evolutionary process, including generalization ability, complexity, diversity, and a recently proposed genotype-phenotype mapping to study the amount of redundancy in trees. The experimental results demonstrate that using any of the two proposed SAM adaptations in TGP allows (i) a significant reduction of tree sizes in the population and (ii) a decrease in redundancy of the trees. When assessed on real-world benchmarks, the generalization ability of the elite solutions does not deteriorate.

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• Published: 06 May 2024

APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease

• Juan Fortea   ORCID: orcid.org/0000-0002-1340-638X 1 , 2 , 3   na1 ,
• Jordi Pegueroles   ORCID: orcid.org/0000-0002-3554-2446 1 , 2 ,
• Daniel Alcolea   ORCID: orcid.org/0000-0002-3819-3245 1 , 2 ,
• Olivia Belbin   ORCID: orcid.org/0000-0002-6109-6371 1 , 2 ,
• Oriol Dols-Icardo   ORCID: orcid.org/0000-0003-2656-8748 1 , 2 ,
• Lídia Vaqué-Alcázar 1 , 4 ,
• Laura Videla   ORCID: orcid.org/0000-0002-9748-8465 1 , 2 , 3 ,
• Juan Domingo Gispert 5 , 6 , 7 , 8 , 9 ,
• Marc Suárez-Calvet   ORCID: orcid.org/0000-0002-2993-569X 5 , 6 , 7 , 8 , 9 ,
• Sterling C. Johnson   ORCID: orcid.org/0000-0002-8501-545X 10 ,
• Reisa Sperling   ORCID: orcid.org/0000-0003-1535-6133 11 ,
• Alexandre Bejanin   ORCID: orcid.org/0000-0002-9958-0951 1 , 2 ,
• Alberto Lleó   ORCID: orcid.org/0000-0002-2568-5478 1 , 2 &
• Víctor Montal   ORCID: orcid.org/0000-0002-5714-9282 1 , 2 , 12   na1  

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• Alzheimer's disease
• Predictive markers

This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer’s disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer’s Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.

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Exceptionally low likelihood of Alzheimer’s dementia in APOE2 homozygotes from a 5,000-person neuropathological study

Association of APOE e2 genotype with Alzheimer’s and non-Alzheimer’s neurodegenerative pathologies

Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies

Data availability.

Access to tabular data from ADNI ( https://adni.loni.usc.edu/ ), OASIS ( https://oasis-brains.org/ ), A4 ( https://ida.loni.usc.edu/collaboration/access/appLicense.jsp ) and NACC ( https://naccdata.org/ ) can be requested online, as publicly available databases. All requests will be reviewed by each studyʼs scientific board. Concrete inquiries to access the WRAP ( https://wrap.wisc.edu/data-requests-2/ ) and ALFA + ( https://www.barcelonabeta.org/en/alfa-study/about-the-alfa-study ) cohort data can be directed to each study team for concept approval and feasibility consultation. Requests will be reviewed to verify whether the request is subject to any intellectual property.

Code availability

All statistical analyses and raw figures were generated using R (v.4.2.2). We used the open-sourced R packages of ggplot2 (v.3.4.3), dplyr (v.1.1.3), ggstream (v.0.1.0), ggpubr (v.0.6), ggstatsplot (v.0.12), Rmisc (v.1.5.1), survival (v.3.5), survminer (v.0.4.9), gtsummary (v.1.7), epitools (v.0.5) and statsExpression (v.1.5.1). Rscripts to replicate our findings can be found at https://gitlab.com/vmontalb/apoe4-asdad (ref. 32 ). For neuroimaging analyses, we used Free Surfer (v.6.0) and ANTs (v.2.4.0).

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Acknowledgements

We acknowledge the contributions of several consortia that provided data for this study. We extend our appreciation to the NACC, the Alzheimer’s Disease Neuroimaging Initiative, The A4 Study, the ALFA Study, the Wisconsin Register for Alzheimer’s Prevention and the OASIS3 Project. Without their dedication to advancing Alzheimer’s disease research and their commitment to data sharing, this study would not have been possible. We also thank all the participants and investigators involved in these consortia for their tireless efforts and invaluable contributions to the field. We also thank the institutions that funded this study, the Fondo de Investigaciones Sanitario, Carlos III Health Institute, the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas and the Generalitat de Catalunya and La Caixa Foundation, as well as the NIH, Horizon 2020 and the Alzheimer’s Association, which was crucial for this research. Funding: National Institute on Aging. This study was supported by the Fondo de Investigaciones Sanitario, Carlos III Health Institute (INT21/00073, PI20/01473 and PI23/01786 to J.F., CP20/00038, PI22/00307 to A.B., PI22/00456 to M.S.-C., PI18/00435 to D.A., PI20/01330 to A.L.) and the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas Program 1, partly jointly funded by Fondo Europeo de Desarrollo Regional, Unión Europea, Una Manera de Hacer Europa. This work was also supported by the National Institutes of Health grants (R01 AG056850; R21 AG056974, R01 AG061566, R01 AG081394 and R61AG066543 to J.F., S10 OD025245, P30 AG062715, U54 HD090256, UL1 TR002373, P01 AG036694 and P50 AG005134 to R.S.; R01 AG027161, R01 AG021155, R01 AG037639, R01 AG054059; P50 AG033514 and P30 AG062715 to S.J.) and ADNI (U01 AG024904), the Department de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca I Innovació en Salut (SLT006/17/00119 to J.F.; SLT002/16/00408 to A.L.) and the A4 Study (R01 AG063689, U24 AG057437 to R.A.S). It was also supported by Fundación Tatiana Pérez de Guzmán el Bueno (IIBSP-DOW-2020-151 o J.F.) and Horizon 2020–Research and Innovation Framework Programme from the European Union (H2020-SC1-BHC-2018-2020 to J.F.; 948677 and 847648 to M.S.-C.). La Caixa Foundation (LCF/PR/GN17/50300004 to M.S.-C.) and EIT Digital (Grant 2021 to J.D.G.) also supported this work. The Alzheimer Association also participated in the funding of this work (AARG-22-923680 to A.B.) and A4/LEARN Study AA15-338729 to R.A.S.). O.D.-I. receives funding from the Alzheimer’s Association (AARF-22-924456) and the Jerome Lejeune Foundation postdoctoral fellowship.

Author information

These authors contributed equally: Juan Fortea, Víctor Montal.

Authors and Affiliations

Sant Pau Memory Unit, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Lídia Vaqué-Alcázar, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain

Juan Fortea & Laura Videla

Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

Lídia Vaqué-Alcázar

Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain

Juan Domingo Gispert & Marc Suárez-Calvet

Neurosciences Programme, IMIM - Hospital del Mar Medical Research Institute, Barcelona, Spain

Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain

Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina. Instituto de Salud carlos III, Madrid, Spain

Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA

Sterling C. Johnson

Brigham and Women’s Hospital Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Reisa Sperling

Barcelona Supercomputing Center, Barcelona, Spain

Víctor Montal

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Contributions

J.F. and V.M. conceptualized the research project and drafted the initial manuscript. V.M., J.P. and J.F. conducted data analysis, interpreted statistical findings and created visual representations of the data. O.B. and O.D.-I. provided valuable insights into the genetics of APOE. L.V., A.B. and L.V.-A. meticulously reviewed and edited the manuscript for clarity, accuracy and coherence. J.D.G., M.S.-C., S.J. and R.S. played pivotal roles in data acquisition and securing funding. A.L. and D.A. contributed to the study design, offering guidance and feedback on statistical analyses, and provided critical review of the paper. All authors carefully reviewed the manuscript, offering pertinent feedback that enhanced the study’s quality, and ultimately approved the final version.

Corresponding authors

Correspondence to Juan Fortea or Víctor Montal .

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Competing interests.

S.C.J. has served at scientific advisory boards for ALZPath, Enigma and Roche Diagnostics. M.S.-C. has given lectures in symposia sponsored by Almirall, Eli Lilly, Novo Nordisk, Roche Diagnostics and Roche Farma, received consultancy fees (paid to the institution) from Roche Diagnostics and served on advisory boards of Roche Diagnostics and Grifols. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research & Development and Roche Diagnostics. J.D.G. has served as consultant for Roche Diagnostics, receives research funding from Hoffmann–La Roche, Roche Diagnostics and GE Healthcare, has given lectures in symposia sponsored by Biogen, Philips Nederlands, Esteve and Life Molecular Imaging and serves on an advisory board for Prothena Biosciences. R.S. has received personal consulting fees from Abbvie, AC Immune, Acumen, Alector, Bristol Myers Squibb, Janssen, Genentech, Ionis and Vaxxinity outside the submitted work. O.B. reported receiving personal fees from Adx NeuroSciences outside the submitted work. D.A. reported receiving personal fees for advisory board services and/or speaker honoraria from Fujirebio-Europe, Roche, Nutricia, Krka Farmacéutica and Esteve, outside the submitted work. A.L. has served as a consultant or on advisory boards for Almirall, Fujirebio-Europe, Grifols, Eisai, Lilly, Novartis, Roche, Biogen and Nutricia, outside the submitted work. J.F. reported receiving personal fees for service on the advisory boards, adjudication committees or speaker honoraria from AC Immune, Adamed, Alzheon, Biogen, Eisai, Esteve, Fujirebio, Ionis, Laboratorios Carnot, Life Molecular Imaging, Lilly, Lundbeck, Perha, Roche and outside the submitted work. O.B., D.A., A.L. and J.F. report holding a patent for markers of synaptopathy in neurodegenerative disease (licensed to Adx, EPI8382175.0). The remaining authors declare no competing interests.

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Fortea, J., Pegueroles, J., Alcolea, D. et al. APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease. Nat Med (2024). https://doi.org/10.1038/s41591-024-02931-w

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Study Suggests Genetics as a Cause, Not Just a Risk, for Some Alzheimer’s

People with two copies of the gene variant APOE4 are almost certain to get Alzheimer’s, say researchers, who proposed a framework under which such patients could be diagnosed years before symptoms.

By Pam Belluck

Scientists are proposing a new way of understanding the genetics of Alzheimer’s that would mean that up to a fifth of patients would be considered to have a genetically caused form of the disease.

Currently, the vast majority of Alzheimer’s cases do not have a clearly identified cause. The new designation, proposed in a study published Monday, could broaden the scope of efforts to develop treatments, including gene therapy, and affect the design of clinical trials.

It could also mean that hundreds of thousands of people in the United States alone could, if they chose, receive a diagnosis of Alzheimer’s before developing any symptoms of cognitive decline, although there currently are no treatments for people at that stage.

The new classification would make this type of Alzheimer’s one of the most common genetic disorders in the world, medical experts said.

“This reconceptualization that we’re proposing affects not a small minority of people,” said Dr. Juan Fortea, an author of the study and the director of the Sant Pau Memory Unit in Barcelona, Spain. “Sometimes we say that we don’t know the cause of Alzheimer’s disease,” but, he said, this would mean that about 15 to 20 percent of cases “can be tracked back to a cause, and the cause is in the genes.”

The idea involves a gene variant called APOE4. Scientists have long known that inheriting one copy of the variant increases the risk of developing Alzheimer’s, and that people with two copies, inherited from each parent, have vastly increased risk.

The new study , published in the journal Nature Medicine, analyzed data from over 500 people with two copies of APOE4, a significantly larger pool than in previous studies. The researchers found that almost all of those patients developed the biological pathology of Alzheimer’s, and the authors say that two copies of APOE4 should now be considered a cause of Alzheimer’s — not simply a risk factor.

The patients also developed Alzheimer’s pathology relatively young, the study found. By age 55, over 95 percent had biological markers associated with the disease. By 65, almost all had abnormal levels of a protein called amyloid that forms plaques in the brain, a hallmark of Alzheimer’s. And many started developing symptoms of cognitive decline at age 65, younger than most people without the APOE4 variant.

“The critical thing is that these individuals are often symptomatic 10 years earlier than other forms of Alzheimer’s disease,” said Dr. Reisa Sperling, a neurologist at Mass General Brigham in Boston and an author of the study.

She added, “By the time they are picked up and clinically diagnosed, because they’re often younger, they have more pathology.”

People with two copies, known as APOE4 homozygotes, make up 2 to 3 percent of the general population, but are an estimated 15 to 20 percent of people with Alzheimer’s dementia, experts said. People with one copy make up about 15 to 25 percent of the general population, and about 50 percent of Alzheimer’s dementia patients.

The most common variant is called APOE3, which seems to have a neutral effect on Alzheimer’s risk. About 75 percent of the general population has one copy of APOE3, and more than half of the general population has two copies.

Alzheimer’s experts not involved in the study said classifying the two-copy condition as genetically determined Alzheimer’s could have significant implications, including encouraging drug development beyond the field’s recent major focus on treatments that target and reduce amyloid.

Dr. Samuel Gandy, an Alzheimer’s researcher at Mount Sinai in New York, who was not involved in the study, said that patients with two copies of APOE4 faced much higher safety risks from anti-amyloid drugs.

When the Food and Drug Administration approved the anti-amyloid drug Leqembi last year, it required a black-box warning on the label saying that the medication can cause “serious and life-threatening events” such as swelling and bleeding in the brain, especially for people with two copies of APOE4. Some treatment centers decided not to offer Leqembi, an intravenous infusion, to such patients.

Dr. Gandy and other experts said that classifying these patients as having a distinct genetic form of Alzheimer’s would galvanize interest in developing drugs that are safe and effective for them and add urgency to current efforts to prevent cognitive decline in people who do not yet have symptoms.

“Rather than say we have nothing for you, let’s look for a trial,” Dr. Gandy said, adding that such patients should be included in trials at younger ages, given how early their pathology starts.

Besides trying to develop drugs, some researchers are exploring gene editing to transform APOE4 into a variant called APOE2, which appears to protect against Alzheimer’s. Another gene-therapy approach being studied involves injecting APOE2 into patients’ brains.

The new study had some limitations, including a lack of diversity that might make the findings less generalizable. Most patients in the study had European ancestry. While two copies of APOE4 also greatly increase Alzheimer’s risk in other ethnicities, the risk levels differ, said Dr. Michael Greicius, a neurologist at Stanford University School of Medicine who was not involved in the research.

“One important argument against their interpretation is that the risk of Alzheimer’s disease in APOE4 homozygotes varies substantially across different genetic ancestries,” said Dr. Greicius, who cowrote a study that found that white people with two copies of APOE4 had 13 times the risk of white people with two copies of APOE3, while Black people with two copies of APOE4 had 6.5 times the risk of Black people with two copies of APOE3.

“This has critical implications when counseling patients about their ancestry-informed genetic risk for Alzheimer’s disease,” he said, “and it also speaks to some yet-to-be-discovered genetics and biology that presumably drive this massive difference in risk.”

Under the current genetic understanding of Alzheimer’s, less than 2 percent of cases are considered genetically caused. Some of those patients inherited a mutation in one of three genes and can develop symptoms as early as their 30s or 40s. Others are people with Down syndrome, who have three copies of a chromosome containing a protein that often leads to what is called Down syndrome-associated Alzheimer’s disease .

Dr. Sperling said the genetic alterations in those cases are believed to fuel buildup of amyloid, while APOE4 is believed to interfere with clearing amyloid buildup.

Under the researchers’ proposal, having one copy of APOE4 would continue to be considered a risk factor, not enough to cause Alzheimer’s, Dr. Fortea said. It is unusual for diseases to follow that genetic pattern, called “semidominance,” with two copies of a variant causing the disease, but one copy only increasing risk, experts said.

The new recommendation will prompt questions about whether people should get tested to determine if they have the APOE4 variant.

Dr. Greicius said that until there were treatments for people with two copies of APOE4 or trials of therapies to prevent them from developing dementia, “My recommendation is if you don’t have symptoms, you should definitely not figure out your APOE status.”

He added, “It will only cause grief at this point.”

Finding ways to help these patients cannot come soon enough, Dr. Sperling said, adding, “These individuals are desperate, they’ve seen it in both of their parents often and really need therapies.”

Pam Belluck is a health and science reporter, covering a range of subjects, including reproductive health, long Covid, brain science, neurological disorders, mental health and genetics. More about Pam Belluck

The Fight Against Alzheimer’s Disease

Alzheimer’s is the most common form of dementia, but much remains unknown about this daunting disease..

How is Alzheimer’s diagnosed? What causes Alzheimer’s? We answered some common questions .

A study suggests that genetics can be a cause of Alzheimer’s , not just a risk, raising the prospect of diagnosis years before symptoms appear.

Determining whether someone has Alzheimer’s usually requires an extended diagnostic process . But new criteria could lead to a diagnosis on the basis of a simple blood test .

The F.D.A. has given full approval to the Alzheimer’s drug Leqembi. Here is what to know about i t.

Alzheimer’s can make communicating difficult. We asked experts for tips on how to talk to someone with the disease .

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Simplifying Debiased Inference via Automatic Differentiation and Probabilistic Programming

Published 5/14/2024

We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known as the efficient influence function. Dimple avoids this task by applying automatic differentiation to the statistical functional of interest. Doing so requires expressing this functional as a composition of primitives satisfying a novel differentiability condition. Dimple also uses this composition to determine the nuisances it must estimate. In software, primitives can be implemented independently of one another and reused across different estimation problems. We provide a proof-of-concept Python implementation and showcase through examples how it allows users to go from parameter specification to efficient estimation with just a few lines of code.

May. 13, 2024

Rice study reveals insights into protein evolution.

Rice University’s Peter Wolynes and his research team have unveiled a breakthrough in understanding how specific genetic sequences, known as pseudogenes, evolve. Their paper was published May 13 by the Proceedings of the National Academy  of Sciences of the United States of America Journal.

Led by Wolynes, the D.R. Bullard-Welch Foundation Professor of Science, professor of chemistry, biosciences and physics and astronomy and co-director of the Center for Theoretical Biological Physics (CTBP), the team focused on deciphering the complex energy landscapes of de-evolved, putative protein sequences corresponding to pseudogenes.

Pseudogenes are segments of DNA that once encoded proteins but have since lost their ability to do so due to sequence degradation — a phenomenon referred to as devolution. Here, devolution represents an unconstrained evolutionary process that occurs without the usual evolutionary pressures that regulate functional protein-coding sequences.

Despite their inactive state, pseudogenes offer a window into the evolutionary journey of proteins.

“Our paper explains that proteins can de-evolve,” Wolynes said. “A DNA sequence can, by mutations or other means, lose the signal that tells it to code for a protein. The DNA continues to mutate but does not have to lead to a sequence that can fold.”

The researchers studied junk DNA in a genome that has de-evolved. Their research revealed that a mutation accumulation in pseudogene sequences typically disrupts the native network of stabilizing interactions, making it challenging for these sequences, if they were to be translated, to fold into functional proteins.

However, the researchers observed instances where certain mutations unexpectedly stabilized the folding of pseudogenes at the cost of altering their previous biological functions.

They identified specific pseudogenes, such as cyclophilin A, profilin-1 and small ubiquitin-like modifier 2 protein, where stabilizing mutations occurred in regions crucial for binding to other molecules and other functions, suggesting a complex balance between protein stability and biological activity.

Moreover, the study highlights the dynamic nature of protein evolution as some previously pseudogenized genes may regain their protein-coding function over time despite undergoing multiple mutations.

Using sophisticated computational models, the researchers interpreted the interplay between physical folding landscapes and the evolutionary landscapes of pseudogenes. Their findings provide evidence that the funnellike character of folding landscapes comes from evolution.

“Proteins can de-evolve and have their ability to fold compromised over time due to mutations or other means,” Wolynes said. “Our study offers the first direct evidence that evolution is shaping the folding of proteins.”

Along with Wolynes, the research team includes lead author and applied physics graduate student Hana Jaafari ; CTBP postdoctoral associate Carlos Bueno ; University of Texas at Dallas graduate student Jonathan Martin; Faruck Morcos, associate professor in the Department of Biological Sciences at UT-Dallas; and CTBP biophysics researcher Nicholas P. Schafer.

The implications of this research extend beyond theoretical biology with potential applications in protein engineering, Jaafari said.

“It would be interesting to see if someone at a lab could confirm our results to see what happens to the pseudogenes that were more physically stable,” Jaafari said. “We have an idea based on our analysis, but it’d be compelling to get some experimental validation.”

More From Forbes

A psychologist explores the ‘politics-genetics link’.

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Research reveals that our political identities are shaped by the intricate dance of nature and ... [+] nurture.

Experiencing turbulent political events, unprecedented changes in climate and novel illnesses in the past few years have shaken global perceptions of stability and security. In these trying times, many turn to their political leaders, hoping they might make decisions and policies that lead to a brighter future.

From staunch conservatism on one end to progressive fervor on the other, our political ideology sways our perceptions and interactions with the world. An intricate web of factors weave together to shape our political identities and we reweave this web constantly to assimilate new experiences and challenges around us.

Here are three factors influencing political ideologies across our lifespan, according to research.

1. Genetic Predispositions

Political beliefs and affiliations are not simply a result of external influences. Biological predispositions and genetic inheritances also play a significant role in dictating our political leanings.

A 2020 study published in Current Opinion in Behavioral Sciences found that political ideology is about 40% heritable. Our genes seem to have a widespread impact on our political identity—affecting how we perceive and react to people different from us, view political events and vote in elections.

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Interestingly, hereditary influences on political orientation are dynamic in nature throughout our lives. Another 2020 study published in the Journal of Personality and Social Psychology found that genetic impact on political orientation was stronger in young adults than in adolescents.

It appears that a complex interplay of nature and nurture sculpts political perceptions as we age and we are more influenced by shared socialization contexts, including family and peer influences, when we are younger.

2. Life Experiences

Among the myriad of factors molding our political identities, significant life events shape the trajectory of our beliefs in an undeniable way. Living through stressful situations can leave a long-lasting impact on our health and consequent behavior.

A 2024 study published in Current Opinion in Behavioral Sciences puts forth the “threat-based neural switch theory” that posits that the stress caused by events such as economic crises and socio-political threats influences political opinion. Such stressful situations increase an individual’s susceptibility to oversimplified political messages—including propaganda and ideological rhetoric.

According to this theory, to cope with threats such as a scarcity of resources, our brain function shifts from goal-directed processes to more simplistic information processing, making us more receptive to intolerant political messaging. This shift can lead to a rise in radicalism during pressing times, further highlighting the profound impact of our environment on political ideologies.

3. Personality Traits

Personality traits intertwine with life experiences and genetic factors to form political beliefs, influencing perspectives on governance, social policies and freedom. A 2020 study published in the Journal of Social and Political Psychology found that personality influences our broad political goals as well as daily political issues, such as income splitting or speed limits on roads.

Greater conscientiousness is associated with a more conservative political orientation. Conservatives tend to value conscientious traits of stability, conformity, tradition, order, structure, authority and display a preference for low complexity. These personality traits motivate them to avoid uncertainty and refute ambiguity to maintain stability.

On the other end, researchers found that greater openness and agreeableness result in a more liberal political orientation. Liberals seem to prefer novelty, flexibility and tolerance for uncertainty and ambiguity.

While genetic predispositions may create a foundation for our political attitudes, it is the interaction with life experiences and personality that shapes and refines these learnings. Understanding these complex factors can offer insight into the diversity of political beliefs and behaviors across the world and prompt further contemplation on the political choices we make everyday.

Are you able to accept opposing views with an open mind? Take this psychological assessment to find out: Warm Tolerance Scale (Interpersonal Tolerance)

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