Genetic Algorithms in Competitive Environments

Abstract

Competition is introduced among the populations of a number of genetic algorithms (GAs) in solving optimization problems. The aim is to adapt the parameters of the GAs, by altering the resources of the system, so as to achieve better solutions. The evolution of the different populations, having different sets of parameters, is controlled at the level of metapopulation, i.e., the union of populations, on the basis of statistics and trends of the evolution of every population. An overall fitness measure is introduced that incorporates a diversity measure and the required resources to rank the populations. The fuzzy outcome of the conflict among the populations guides the evolution of the different GAs toward better solutions in the statistical sense. The proposed scheme is applied to two different problems—a multimodal function with six global and several near-global optima, and a reliability based optimal design of a simple truss. Numerical results are presented, and the robustness and computational efficiency of the proposed scheme are discussed.