Intelligent exploration for genetic algorithms: Using self-organizing maps in evolutionary computation

Heni Ben Amor, Achim Rettinger

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

50 Scopus citations

Abstract

Exploration vs. exploitation is a well known issue in Evolutionary Algorithms. Accordingly, an unbalanced search can lead to premature convergence. GASOM, a novel Genetic Algorithm, addresses this problem by intelligent exploration techniques. The approach uses Self-Organizing Maps to mine data from the evolution process. The information obtained is successfully utilized to enhance the search strategy and confront genetic drift. This way, local optima are avoided and exploratory power is maintained. The evaluation of GASOM on well known problems shows that it effectively prevents premature convergence and seeks the global optimum. Particularly on deceptive and misleading functions it showed outstanding performance. Additionally, representing the search history by the Self-Organizing Map provides a visually pleasing insight into the state and course of evolution.

Original languageEnglish (US)
Title of host publicationGECCO 2005 - Genetic and Evolutionary Computation Conference
EditorsH.G. Beyer, U.M. O'Reilly, D. Arnold, W. Banzhaf, C. Blum, E.W. Bonabeau, E. Cantu-Paz, D. Dasgupta, K. Deb, al et al
Pages1531-1538
Number of pages8
DOIs
StatePublished - Dec 1 2005
Externally publishedYes
EventGECCO 2005 - Genetic and Evolutionary Computation Conference - Washington, D.C., United States
Duration: Jun 25 2005Jun 29 2005

Publication series

NameGECCO 2005 - Genetic and Evolutionary Computation Conference

Conference

ConferenceGECCO 2005 - Genetic and Evolutionary Computation Conference
CountryUnited States
CityWashington, D.C.
Period6/25/056/29/05

Keywords

  • Diversity
  • Exploration vs. Exploitation
  • Genetic Algorithm
  • Genetic Drift
  • Premature Convergence
  • Self-Organizing Map

ASJC Scopus subject areas

  • Engineering(all)

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