GAS, a concept on modeling species in genetic algorithms

Márk Jelasity, József Dombi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper introduces a niching technique called GAS (S stands for species) which dinamically creates a subpopulation structure (taxonomic chart) using a radius function instead of a single radius, and a ‘cooling’ method similar to simulated annealing. GAS offers a solution to the niche radius problem with the help of these techniques. A method based on the speed of species is presented for determining the radius function. Speed functions are given for both real and binary domains. We also discuss the sphere packing problem on binary domains using some tools of coding theory to make it possible to evaluate the output of the system. Finally two problems are examined empirically. The first is a difficult test function with unevenly spread local optima. The second is an NP-complete combinatorial optimization task, where a comparison is presented to the traditional genetic algorithm.

Original languageEnglish
Title of host publicationArtificial Evolution - European Conference, AE 1995, Selected Papers
EditorsJean-Marc Alliot, Evelyne Lutton, Edmund Ronald, Marc Schoenauer, Dominique Snyers
PublisherSpringer Verlag
Pages67-85
Number of pages19
ISBN (Print)3540611088, 9783540611080
DOIs
Publication statusPublished - Jan 1 1996
Event2nd European Conference on Artificial Evolution, AE 1995 - Brest, France
Duration: Sep 4 1995Sep 6 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1063
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd European Conference on Artificial Evolution, AE 1995
CountryFrance
CityBrest
Period9/4/959/6/95

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Jelasity, M., & Dombi, J. (1996). GAS, a concept on modeling species in genetic algorithms. In J-M. Alliot, E. Lutton, E. Ronald, M. Schoenauer, & D. Snyers (Eds.), Artificial Evolution - European Conference, AE 1995, Selected Papers (pp. 67-85). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1063). Springer Verlag. https://doi.org/10.1007/3-540-61108-8_31