Exploring the explorative advantage of the cooperative coevolutionary (1+1) EA

Abstract

Using a well-known cooperative coevolutionary function optimization framework, a very simple cooperative coevolutionary (1+1) EA is defined. This algorithm is investigated in the context of expected optimization time. The focus is on the impact the cooperative coevolutionary approach has and on the possible advantage it may have over more traditional evolutionary approaches. Therefore, a systematic comparison between the expected optimization times of this coevolutionary algorithm and the ordinary (1+1) EA is presented. The main result is that separability of the objective function alone is is not sufficient to make the cooperative coevolutionary approach beneficial. By presenting a clear structured example function and analyzing the algorithms' performance, it is shown that the cooperative coevolutionary approach comes with new explorative possibilities. This can lead to an immense speed-up of the optimization. © Springer-Verlag Berlin Heidelberg 2003.