Autonomous virulence adaptation improves coevolutionary optimization

J Cartlidge, D Ait-Boudaoud

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)
553 Downloads (Pure)

Abstract

A novel approach for the autonomous virulence adaptation (AVA) of competing populations in a coevolutionary optimization framework is presented. Previous work has demonstrated that setting an appropriate virulence, v, of populations accelerates coevolutionary optimization by avoiding detrimental periods of disengagement. However, since the likelihood of disengagement varies both between systems and over time, choosing the ideal value of v is problematic. The AVA technique presented here uses a machine learning approach to continuously tune v as system engagement varies. In a simple, abstract domain, AVA is shown to successfully adapt to the most productive values of v. Further experiments, in more complex domains of sorting networks and maze navigation, demonstrate AVA's efficiency over reduced virulence and the layered Pareto coevolutionary archive.
Translated title of the contributionAutonomous Virulence Adaptation Improves Coevolutionary Optimization
Original languageEnglish
Pages (from-to)215 - 229
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Volume15
Issue number2
DOIs
Publication statusPublished - Apr 2011

Keywords

  • coevolution
  • coevolutionary dynamics
  • disengagement
  • virulence
  • optimisation
  • virulence adaptation
  • maze solving robots
  • sorting networks
  • pareto archive

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