Parameter control in evolutionary algorithms

The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research.

[1]  Bull,et al.  An Overview of Genetic Algorithms: Part 2, Research Topics , 1993 .

[2]  A.E. Eiben,et al.  Competing crossovers in an adaptive GA framework , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[3]  D. Fogel,et al.  A comparison of methods for self-adaptation in evolutionary algorithms. , 1995, Bio Systems.

[4]  Bryant A. Julstrom,et al.  What Have You Done for Me Lately? Adapting Operator Probabilities in a Steady-State Genetic Algorithm , 1995, ICGA.

[5]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Jano I. van Hemert,et al.  Adapting the Fitness Function in GP for Data Mining , 1999, EuroGP.

[7]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[8]  T. Back,et al.  On the behavior of evolutionary algorithms in dynamic environments , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[9]  Dirk Van Gucht,et al.  The effects of population size, heuristic crossover and local improvement on a genetic algorithm for the traveling salesman problem , 1989 .

[10]  Joe Suzuki.  A Markov Chain Analysis on A Genetic Algorithm , 1993, ICGA.

[11]  Thomas Bäck,et al.  Intelligent Mutation Rate Control in Canonical Genetic Algorithms , 1996, ISMIS.

[12]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[13]  David B. Fogel,et al.  A Preliminary Investigation into Directed Mutations in Evolutionary Algorithms , 1996, PPSN.

[14]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[15]  T. Soule,et al.  Using genetic programming to approximate maximum clique , 1996 .

[16]  Joe Suzuki,et al.  A Markov chain analysis on simple genetic algorithms , 1995, IEEE Trans. Syst. Man Cybern..

[17]  William M. Spears,et al.  Adapting Crossover in Evolutionary Algorithms , 1995, Evolutionary Programming.

[18]  Paul Morris,et al.  The Breakout Method for Escaping from Local Minima , 1993, AAAI.

[19]  Robert E. Smith,et al.  Adaptively Resizing Populations: Algorithm, Analysis, and First Results , 1993, Complex Syst..

[20]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[21]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

[22]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[23]  Alice E. Smith,et al.  Expected Allele Coverage and the Role of Mutation in Genetic Algorithms , 1993, ICGA.

[24]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[25]  Anne Brindle,et al.  Genetic algorithms for function optimization , 1980 .

[26]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[27]  J. David Schaffer,et al.  An Adaptive Crossover Distribution Mechanism for Genetic Algorithms , 1987, ICGA.

[28]  A. E. Eiben,et al.  GA-easy and GA-hard Constraint Satisfaction Problems , 1995, Constraint Processing, Selected Papers.

[29]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[30]  Paulien Hogeweg,et al.  Evolutionary Consequences of Coevolving Targets , 1997, Evolutionary Computation.

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  Nikolaus Hansen,et al.  On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation , 1995, ICGA.

[33]  Zbigniew Michalewicz,et al.  A Decoder-Based Evolutionary Algorithm for Constrained Parameter Optimization Problems , 1998, PPSN.

[34]  Terence C. Fogarty,et al.  A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments , 1996, PPSN.

[35]  Thomas Bäck,et al.  An Empirical Study on GAs "Without Parameters" , 2000, PPSN.

[36]  Zbigniew Michalewicz,et al.  Inver-over Operator for the TSP , 1998, PPSN.

[37]  A. E. Eiben,et al.  Self-adaptivity for constraint satisfaction: learning penalty functions , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[38]  Heinz Mühlenbein,et al.  Adaptation of population sizes by competing subpopulations , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[39]  Jan Paredis,et al.  Co-evolutionary Constraint Satisfaction , 1994, PPSN.

[40]  Lawrence Davis,et al.  Adapting Operator Probabilities in Genetic Algorithms , 1989, ICGA.

[41]  Jim Smith,et al.  Adaptively Parameterised Evolutionary Systems: Self-Adaptive Recombination and Mutation in a Genetic Algorithm , 1996, PPSN.

[42]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 , 2000 .

[43]  Terence C. Fogarty,et al.  Learning the local search range for genetic optimisation in nonstationary environments , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[44]  David B. Fogel,et al.  A Comparison of Self-Adaptation Methods for Finite State Machines in Dynamic Environments , 1996, Evolutionary Programming.

[45]  F. Greene.  A method for utilizing diploid/dominance in genetic search , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[46]  Alice E. Smith,et al.  Genetic Optimization Using A Penalty Function , 1993, ICGA.

[47]  Thomas Bäck,et al.  A Comparative Study of a Penalty Function, a Repair Heuristic and Stochastic Operators with the Set-Covering Problem , 1995, Artificial Evolution.

[48]  L. Darrell Whitley,et al.  Delta Coding: An Iterative Search Strategy for Genetic Algorithms , 1991, ICGA.

[49]  Byoung-Tak Zhang,et al.  Balancing Accuracy and Parsimony in Genetic Programming , 1995, Evolutionary Computation.

[50]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[51]  Michael D. Vose,et al.  Modeling Simple Genetic Algorithms , 1995, Evolutionary Computation.

[52]  Thomas Bäck,et al.  A Superior Evolutionary Algorithm for 3-SAT , 1998, Evolutionary Programming.

[53]  Jan Paredis,et al.  The Symbiotic Evolution of Solutions and Their Representations , 1995, International Conference on Genetic Algorithms.

[54]  Zbigniew Michalewicz,et al.  An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms , 1991, ICGA.

[55]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[56]  Jano van Hemert,et al.  SAW-ing EAs: adapting the fitness function for solving constrained problems , 1999 .

[57]  Jim E. Smith.  Self adaptation in evolutionary algorithms , 1998 .

[58]  Peter Ross,et al.  Cost Based Operator Rate Adaption: An Investigation , 1996, PPSN.

[59]  Yukinori Kakazu,et al.  Adaptive Search Strategy for Genetic Algorithms with Additional Genetic Algorithms , 1992, PPSN.

[60]  Robert G. Reynolds,et al.  Evolutionary Programming VI , 1997, Lecture Notes in Computer Science.

[61]  Christopher R. Stephens,et al.  Self-Adaptation in Evolving Systems , 1997, Artificial Life.

[62]  Jim Smith,et al.  A Memetic Algorithm With Self-Adaptive Local Search: TSP as a case study , 2000, GECCO.

[63]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[64]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[65]  James R. Levenick,et al.  Swappers: introns promote flexibility, diversity and invention , 1999 .

[66]  Peter J. Angeline,et al.  Tracking Extrema in Dynamic Environments , 1997, Evolutionary Programming.

[67]  H. IBA,et al.  Recombination guidance for numerical genetic programming , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[68]  Jim Smith,et al.  Recombination strategy adaptation via evolution of gene linkage , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[69]  L. Darrell Whitley,et al.  Remapping Hyperspace During Genetic Search: Canonical Delta Folding , 1992, FOGA.

[70]  John Daniel. Bagley,et al.  The behavior of adaptive systems which employ genetic and correlation algorithms : technical report , 1967 .

[71]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[72]  Ben Paechter,et al.  Solving CSPs using self-adaptive constraint weights: how to prevent EAs from cheating , 2000, GECCO.

[73]  Larry J. Eshelman,et al.  Crossover's Niche , 1993, ICGA.

[74]  Jano I. van Hemert,et al.  Graph Coloring with Adaptive Evolutionary Algorithms , 1998, J. Heuristics.

[75]  L. Darrell Whitley,et al.  Changing Representations During Search: A Comparative Study of Delta Coding , 1994, Evolutionary Computation.

[76]  Zbigniew Michalewicz,et al.  GENOCOP: a genetic algorithm for numerical optimization problems with linear constraints , 1996, CACM.

[77]  F. Greene.  Performance of Diploid Dominance with Genetically Synthesized Signal Processing Networks , 1997, ICGA.

[78]  Dirk Thierens.  Dimensional Analysis of Allele-Wise Mixing Revisited , 1996, PPSN.

[79]  David B. Fogel,et al.  An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines , 1995, Evolutionary Programming.

[80]  Hideyuki Takagi,et al.  Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques , 1993, ICGA.

[81]  Günter Rudolph,et al.  A cellular genetic algorithm with self-adjusting acceptance threshold , 1995 .

[82]  Jim Smith,et al.  Self adaptation of mutation rates in a steady state genetic algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[83]  R. Hinterding,et al.  Gaussian mutation and self-adaption for numeric genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[84]  Terence C. Fogarty,et al.  Varying the Probability of Mutation in the Genetic Algorithm , 1989, ICGA.

[85]  Zbigniew Michalewicz,et al.  Adaptation in evolutionary computation: a survey , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[86]  Tony White,et al.  Adaptive Crossover Using Automata , 1994, PPSN.

[87]  William E. Hart,et al.  Optimizing an Arbitrary Function is Hard for the Genetic Algorithm , 1991 .

[88]  Jan Paredis,et al.  Coevolutionary computation , 1995 .

[89]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[90]  Dirk Thierens,et al.  Toward a Better Understanding of Mixing in Genetic Algorithms , 1993 .

[91]  Joanna Lis,et al.  Parallel genetic algorithm with the dynamic control parameter , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[92]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[93]  Dirk Thierens,et al.  Mixing in Genetic Algorithms , 1993, ICGA.

[94]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[95]  Hugh M. Cartwright,et al.  Looking Around: Using Clues from the Data Space to Guide Genetic Algorithm Searches , 1991, ICGA.

[96]  James Bowen,et al.  Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[97]  Peter J. Angeline,et al.  Two self-adaptive crossover operators for genetic programming , 1996 .

[98]  Robert E. Smith,et al.  Adaptively Resizing Populations: An Algorithm and Analysis , 1993, ICGA.

[99]  Akira Oyama,et al.  Real-Coded Adaptive Range Genetic Algorithm Applied to Transonic Wing Optimization , 2000, PPSN.

[100]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[101]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[102]  A. Eiben,et al.  Solving 3-SAT by GAs adapting constraint weights , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[103]  Robert G. Reynolds,et al.  Adapting Crossover in Evolutionary Algorithms , 1995 .

[104]  D. Fogel,et al.  Case studies in applying fitness distributions in evolutionary algorithms. II. Comparing the improvements from crossover and Gaussian mutation on simple neural networks , 2000, 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00.

[105]  Heinz Mühlenbein,et al.  Strategy Adaption by Competing Subpopulations , 1994, PPSN.

[106]  A. E. Eiben,et al.  Adaptive Penalties for Evolutionary Graph Coloring , 1997, Artificial Evolution.

[107]  David E. Goldberg,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1999, Evolutionary Computation.

[108]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[109]  Elena Marchiori,et al.  Solving Binary Constraint Satisfaction Problems Using Evolutionary Algorithms with an Adaptive Fitness Function , 1998, PPSN.

[110]  Xin Yao,et al.  An Analysis of Evolutionary Algorithms Based on Neighborhood and Step Sizes , 1997, Evolutionary Programming.

[111]  Reinhard Männer,et al.  Towards an Optimal Mutation Probability for Genetic Algorithms , 1990, PPSN.

[112]  R. Hinterding.  Self-adaptation using multi-chromosomes , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[113]  Zbigniew Michalewicz,et al.  Self-Adaptive Genetic Algorithm for Numeric Functions , 1996, PPSN.

[114]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[115]  Thomas Bäck,et al.  Optimal Mutation Rates in Genetic Search , 1993, ICGA.

[116]  Wolfgang Banzhaf,et al.  Empirical analysis of different levels of meta-evolution , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[117]  James Bowen,et al.  Solving randomly generated constraint satisfaction problems using a micro-evolutionary hybrid that evolves a population of hill-climbers , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[118]  Annie S. Wu,et al.  Empirical Studies of the Genetic Algorithm with Noncoding Segments , 1995, Evolutionary Computation.

[119]  Thomas Bäck,et al.  The Interaction of Mutation Rate, Selection, and Self-Adaptation Within a Genetic Algorithm , 1992, PPSN.

[120]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[121]  Kalyanmoy Deb,et al.  Analysis of Selection Algorithms: A Markov Chain Approach , 1996, Evolutionary Computation.

[122]  C. G. Shaefer,et al.  The ARGOT Strategy: Adaptive Representation Genetic Optimizer Technique , 1987, ICGA.

[123]  Jim Smith,et al.  Operator and parameter adaptation in genetic algorithms , 1997, Soft Comput..

[124]  David E. Goldberg,et al.  Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy , 1987, ICGA.

[125]  Kalyanmoy Deb,et al.  Accounting for Noise in the Sizing of Populations , 1992, FOGA.

[126]  Larry J. Eshelman,et al.  On Crossover as an Evolutionarily Viable Strategy , 1991, ICGA.

[127]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization , 1999, Evolutionary Computation.