Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
Genetic Programming and Evolvable Machines
GP under streaming data constraints: a case for pareto archiving?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Semantic bias in program coevolution
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.