Autonomous virulence adaptation improves coevolutionary optimization

J. Cartlidge, Djamel Ait-Boudaoud

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)215-229
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Issue number2
Publication statusPublished - Apr 2011


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