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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 beendoi:10.1145/1143997.1144062 dblp:conf/gecco/VanneschiMVC06 fatcat:fwd7srfukbdmdozkx3mx6ex54u