Crossover, Macromutationand, and Population-Based Search

A Genetic Algorithm (GA) maintains a population of individuals for the express purpose of improving performance via communication of information between contemporary individuals. This is achieved in a GA through the use of a crossover operator. If crossover is not a useful method for this exchange, the GA should not, on average, perform any better than a variety of simpler algorithms that are not population-based. A simple method for testing the usefulness of crossover for a particular problem is presented. This makes it possible to identify situations in which crossover is apparently useful but is in fact producing gains that are only equal to (or less than) those that can be obtained with macromutation and no population.

[1]  T. Rado.  On non-computable functions , 1962 .

[2]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[3]  R. Rosenberg.  Simulation of genetic populations with biochemical properties : technical report , 1967 .

[4]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[5]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[6]  J. D. Schaffer,et al.  Multiple Objective Optimization with Vector Evaluated Genetic Algorithms , 1985, ICGA.

[7]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[8]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[9]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[10]  D. Ackley.  A connectionist machine for genetic hillclimbing , 1987 .

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

[12]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[13]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

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

[15]  Jack Sklansky,et al.  Constrained Genetic Optimization via Dynarnic Reward-Penalty Balancing and Its Use in Pattern Recognition , 1989, ICGA.

[16]  Kenneth A. De Jong,et al.  An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms , 1990, PPSN.

[17]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[18]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[19]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[20]  Zbigniew Michalewicz,et al.  Handling Constraints in Genetic Algorithms , 1991, ICGA.

[21]  Thomas Bäck,et al.  Extended Selection Mechanisms in Genetic Algorithms , 1991, ICGA.

[22]  Yoshiakira Akimoto,et al.  Genetic algorithms approach to voltage optimization , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[23]  P. Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

[24]  Kennetb A. De.  Genetic Algorithms Are NOT Function Optimizers , 1992 .

[25]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[26]  Gregory J. E. Rawlins,et al.  Reverse HillclimbingGenetic Algorithms and the Busy Beaver Problem , 1993, ICGA.

[27]  Peter J. Angeline,et al.  Competitive Environments Evolve Better Solutions for Complex Tasks , 1993, ICGA.

[28]  Kenji Iba.  Reactive power optimization by genetic algorithm , 1993 .

[29]  Michael M. Skolnick,et al.  Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints , 1993, ICGA.

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

[31]  Marc Schoenauer,et al.  Constrained GA Optimization , 1993, ICGA.

[32]  Shu-Yuen Hwang,et al.  A Genetic Algorithm with Disruptive Selection , 1993, ICGA.

[33]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[34]  D. B. Fogel,et al.  Applying evolutionary programming to selected control problems , 1994 .

[35]  Anthony V. Sebald,et al.  Minimax design of neural net controllers for highly uncertain plants , 1994, IEEE Trans. Neural Networks.

[36]  L. C. Stayton,et al.  On the effectiveness of crossover in simulated evolutionary optimization. , 1994, Bio Systems.

[37]  Terry Jones,et al.  A Description of Holland's Royal Road Function , 1994, Evolutionary Computation.

[38]  Joseph C. Culberson,et al.  Mutation-Crossover Isomorphisms and the Construction of Discriminating Functions , 1994, Evolutionary Computation.

[39]  Y.-Y. Hsu,et al.  Reactive power/voltage control in a distribution substation using dynamic programming , 1995 .

[40]  Tim Jones.  Evolutionary Algorithms, Fitness Landscapes and Search , 1995 .

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

[42]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[43]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[44]  S. Ronald,et al.  Robust encodings in genetic algorithms: a survey of encoding issues , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[45]  La regulation de tension des reseaux publics de distribution d'electricite , 1997 .

[46]  Michael Pilegaard Hansen,et al.  Tabu Search for Multiobjective Optimization: MOTS , 1997 .