On the improvement of coevolutionary optimizers by learning variable interdependencies

Abstract

During the last years, cooperating coevolutionary algorithms could improve the convergence of several optimization benchmarks significantly by placing each dimension of the search space in its own subpopulation. However, their general applicability is restricted by problems with epistatic links between problem dimensions, a major obstacle in cooperating coevolutionary function optimization. The work presents first preliminary studies on a technique to recognize epistatic links in problems and self-adapt the algorithm in such a way that populations with interrelated dimensions are merged to a common population.