Optimal Mutation Rates in Genetic Search

The optimization of a single bit string by means of iterated mutation and selection of the best a Genetic Algorithm is dis cussed with respect to three simple tness functions The counting ones problem a standard binary encoded integer and a Gray coded integer optimization problem A mu tation rate schedule that is optimal with re spect to the success probability of mutation is presented for each of the objective functions and it turns out that the standard binary code can hamper the search process even in case of unimodal objective functions While normally a mutation rate of l where l de notes the bit string length is recommend able our results indicate that a variation of the mutation rate is useful in cases where the tness function is a multimodal pseudo boolean function where multimodality may be caused by the objective function as well as the encoding mechanism

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

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

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

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

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

[6]  Roe Goodman,et al.  Introduction to stochastic models , 1987 .

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

[8]  Eugene Semenkin,et al.  Optimization of unimodal monotone pseudoboolean functions , 1990, Kybernetika.

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

[10]  J. David Schaffer,et al.  Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms , 1988, ML.

[11]  Schloss Birlinghoven,et al.  How Genetic Algorithms Really Work I.mutation and Hillclimbing , 2022 .

[12]  Albert Donally Bethke,et al.  Genetic Algorithms as Function Optimizers , 1980 .

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

[14]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.