A cooperative coevolutionary algorithm with correlation based adaptive variable partitioning

Authors:
Tapabrata Ray;Xin Yao
Affiliations:
School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT, Australia;The Centre of Excellence for Research in Computational Intelligence and Applications, School of Computer Science, University of Birmingham, United Kingdom
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

A cooperative coevolutionary algorithm (CCEA) is an extension to an evolutionary algorithm (EA); it employs a divide and conquer strategy to solve an optimization problem. In its basic form, a CCEA splits the variables of an optimization problem into multiple smaller subsets and evolves them independently in different subpopulations. The dynamics of a CCEA is far more complex than an EA and its performance can vary from good to bad depending on the separability of the optimization problem. This paper provides some insights into why CCEA in its basic form is not suitable for nonseparable problems and introduces a Cooperative Coevolutionary Algorithm with Correlation based Adaptive Variable Partitioning (CCEA-AVP) to deal with such problems. The performance of CCEA-AVP is compared with CCEA and EA to highlight its benefits. CCEA-AVP offers the possibility to deal with problems where separability among variables might vary in different regions of the search space.