Coevolutionary architectures with straight line programs for solving the symbolic regression problem

Abstract

To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. In this paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program and an evolution strategy. As main difference with work previously done, our genetic program evolves straight line programs representing functional expressions, instead of tree structures. The evolution strategy searches for good values for the numerical terminal symbols used by those expressions. Experimentation has been performed over symbolic regression problem instances and the obtained results have been compared with those obtained by means of Genetic Programming strategies without coevolution. The results show that our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than traditional genetic programming using the same amount of computational effort.