Autonomous Virulence Adaptation Improves Coevolutionary Optimization

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.