Spatial Embedding and Loss of Gradient in Cooperative Coevolutionary Algorithms

Coevolutionary algorithms offer great promise as adaptive problem solvers but suffer from several known pathologies. Historically, spatially embedded coevolutionary algorithms seem to have succeeded where other coevolutionary approaches fail; however, explanations for this have been largely unexplored. We examine this idea more closely by looking at spatial models in the context of a particular coevolutionary pathology: loss of gradient. We believe that loss of gradient in cooperative coevolution is caused by asymmetries in the problem or initial conditions between populations, driving one population to convergence before another. Spatial models seem to lock populations together in terms of evolutionary change, helping establish a type of dynamic balance to thwart loss of gradient. We construct a tunably asymmetric function optimization problem domain and conduct an empirical study to justify this assertion. We find that spatial restrictions for collaboration and selection can help keep population changes balanced when presented with severe asymmetries in the problem.