Co-optimization algorithms

Authors:
Travis C. Service;Daniel R. Tauritz
Affiliations:
Missouri University of Science and Technology, Rolla, MO, USA;Missouri University of Science and Technology, Rolla, MO, USA
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

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Abstract

While coevolution has many parallels to natural evolution, methods other than those based on evolutionary principles may be used in the interactive fitness setting. In this paper we present a generalization of coevolution to co-optimization which allows arbitrary black-box function optimization techniques to be used in a coevolutionary like manner. We find that the co-optimization versions of gradient ascent and simulated annealing are capable of outperforming the canonical coevolutionary algorithm. We also hypothesize that techniques which employ non-population based selection mechanisms are less sensitive to disengagement.