A gene based adaptive mutation strategy for genetic algorithms

Sima Uyar*, Sanem Sariel, Gulsen Eryigit

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Citations (Scopus)

Abstract

In this study, a new mechanism that adapts the mutation rate for each locus on the chromosomes, based on feedback obtained from the current population is proposed. Through tests using the one-max problem, it is shown that the proposed scheme improves convergence rate. Further tests are performed using the 4-Peaks and multiple knapsack test problems to compare the performance of the proposed approach with other similar parameter control approaches. A convergence control scheme that provides acceptable performance is chosen to maintain sufficient diversity in the population and implemented for all tested methods to provide fair comparisons. The effects of using a convergence control mechanism are not within the scope of this paper and will be explored in a future study. As a result of the tests, promising results which promote further experimentation are obtained.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsRiccardo Poli, Owen Holland, Wolfgang Banzhaf, Hans-Georg Beyer, Edmund Burke, Paul Darwen, Dipankar Dasgupta, Dario Floreano, James Foster, Mark Harman, Pier Luca Lanzi, Lee Spector, Andrea G. B. Tettamanzi, Dirk Thierens, Andrew M. Tyrrell
PublisherSpringer Verlag
Pages271-281
Number of pages11
ISBN (Print)3540223436
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3103
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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