Niching the CMA-ES via nearest-better clustering

Authors:
Mike Preuss
Affiliations:
Technische Universität Dortmund, Germany, Dortmund, Germany
Venue:
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Year:
2010

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Abstract

We investigate how a niching based evolutionary algorithm fares on the BBOB function test set, knowing that most problems are not very well suited to this algorithm class. However, as the CMA-ES is included as basic local search algorithm, the niching approach still performs fairly well, with some potential to improve. Basin identification is done via the heuristic nearest-best clustering scheme.