Sequential versus Parallel Cooperative Coevolutionary Algorithms for Optimization
Elena Popovici, K. De Jong · 2006
There continues to be a growing interest in the use of coevolutionary algorithms (CoEAs) to solve difficult computational problems. In particular, cooperative CoEAs are often used for optimization by means of problem decomposition. In addition to the parameters of traditional evolutionary algorithms (EAs), CoEAs have a set of coevolution specific parameters whose values can greatly influence performance. In this paper we study the effects on optimization performance of a parameter called update timing, which controls whether the CoEA runs its subcomponents sequentially or in parallel. This has been studied in [T. Jansen and R. P. Wiegand. Sequential versus parallel cooperative coevolutionary (1+1) EAs. In Proceedings of the IEEE International Congress on Evolutionary Computation. IEEE Press, 2003.] for pseudo-boolean functions. By contrast, we perform the analysis for functions defined on continuous real-number domains. We show the performance effects to be dependent on a problem property called best-response curves and use dynamics analysis to explain this dependency.