Cooperative coevolution has proven to be a promising technique for solving complex combinatorial optimization problems. In this paper, we present four different strategies which involve cooperative coevolution of a genetic program and of a population of constants evolved by a genetic algorithm. The genetic program evolves expressions that solve a problem, while the genetic algorithm provides “good” values for the numeric terminal symbols used by those expressions. Experiments have been performed on three symbolic regression problems and on a “real-world” biomedical application. Results are encouraging and confirm that our coevolutionary algorithms can be used effectively in different domains
Vanneschi, L., Mauri, G., Valsecchi, A., Cagnoni, S. (2006). Heterogeneous cooperative coevolution: Strategies of integration between GP and GA. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, Gecco 2006 (pp.361-368). New York : ACM Press [10.1145/1143997.1144062].
Heterogeneous cooperative coevolution: Strategies of integration between GP and GA
VANNESCHI, LEONARDO;MAURI, GIANCARLO;
2006
Abstract
Cooperative coevolution has proven to be a promising technique for solving complex combinatorial optimization problems. In this paper, we present four different strategies which involve cooperative coevolution of a genetic program and of a population of constants evolved by a genetic algorithm. The genetic program evolves expressions that solve a problem, while the genetic algorithm provides “good” values for the numeric terminal symbols used by those expressions. Experiments have been performed on three symbolic regression problems and on a “real-world” biomedical application. Results are encouraging and confirm that our coevolutionary algorithms can be used effectively in different domainsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.