Combating coevolutionary disengagement by reducing parasite virulence

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

41 Citations (Scopus)
407 Downloads (Pure)

Abstract

While standard evolutionary algorithms employ a static, absolute fitness metric, coevolutionary algorithms assess individuals by their performance relative to populations of opponents that are themselves evolving. Although this arrangement offers the possibility of avoiding long-standing difficulties such as premature convergence, it suffers from its own unique problems, cycling, over-focusing and disengagement. Here, we introduce a novel technique for dealing with the third and least explored of these problems. Inspired by studies of natural host-parasite systems, we show that disengagement can be avoided by selecting for individuals that exhibit reduced levels of "virulence", rather than maximum ability to defeat coevolutionary adversaries. Experiments in both simple and complex domains are used to explain how this counterintuitive approach may be used to improve the success of coevolutionary algorithms.
Original languageEnglish
Pages (from-to)193-222
Number of pages30
JournalEvolutionary Computation
Volume12
Issue number2
Early online date13 Mar 2004
DOIs
Publication statusPublished - Jul 2004

Keywords

  • evolutionary computation
  • coevolution
  • reduced virulence
  • sorting networks
  • optimisation
  • disengagement

Fingerprint Dive into the research topics of 'Combating coevolutionary disengagement by reducing parasite virulence'. Together they form a unique fingerprint.

Cite this