Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
Improvement of the Convergence of Genetic Algorithm by the Learning Function of Stochastic Automata
Makoto HIROYASUShinzo KITAMURA
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1995 Volume 8 Issue 8 Pages 374-380

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Abstract

It has been reported that after prolonged starvation, bacterial cells increase in the frequency of mutation and produce new advantageous phenotypes. This result gives a suggestion for the improvement of the genetic algorithm for optimization problems. The stochastic automaton is applied for the learning of the position of loci at chromosome so that the evaluation takes a higher value. The state stochastic vector of the automaton thus obtained generates a mutation rate for corresponding locus of the chromosome. This procedure helps the algorithm to escape from the trapping in local minima. The effectiveness is shown especially for the maximum search problem of a variable-separable multi-peak function.

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