Co-optimization algorithms

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. Categories and Subject Descriptors

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Travis C. Service, Daniel R. Tauritz
• Keywords: Arbitrary Black-box Function, Canonical Coevolutionary Algorithm, Coevolution, GECCO 2008, Optimization