Preference-inspired co-evolutionary algorithm using weights for many-objective optimization

Authors:
Rui Wang;Robin C. Purshouse;Peter J. Fleming
Affiliations:
Department of Automatic Control and Systems Engineering,The University of Sheffield, Chang sha, China;The University of Sheffield, Sheffield, United Kingdom;The University of Sheffield, Sheffield, United Kingdom
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

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Abstract

Decomposition based approaches are known to perform well on many-objective problems when a suitable set of weights is provided. However, providing a suitable set of weights \textit{a priori} is difficult. This study proposes a novel algorithm: preference-inspired co-evolutionary algorithm using weights (PICEA-w), which co-evolves a set of weights with the usual population of candidate solutions during the search process. The co-evolution enables suitable sets of weights to be constructed along the optimization process, thus guiding the candidate solutions toward the Pareto optimal front. Experimental results show PICEA-w performs better than algorithms embedded with random or uniform weights.