Experimental study of multipopulation parallel genetic programming

Abstract

The parallel execution of several populations in evolutionary algorithms has usually given good results. Nevertheless, researchers have to date drawn conflicting conclusions when using some of the parallel genetic programming models. One aspect of the conflict is population size, since published GP works do not agree about whether to use large or small populations. This paper presents an experimental study of a number of common GP test problems. Via our experiments, we discovered that an optimal range of values exists. This assists us in our choice of population size and in the selection of an appropriate parallel genetic programming model. Finding efficient parameters helps us to speed up our search for solutions. At the same time, it allows us to locate features that are common to parallel genetic programming and the classic genetic programming technique.