Extension of Genetic Programming with Multiple Trees for Agent Learning
Takashi Ito, Keníchi Takahashi, Michimasa Inaba · Journal of Computers · 2016
This paper proposes an extension of genetic programming (GP) with multiple trees.In order to improve the performance, GP with control node (GPCN) and its three kinds of modification have been proposed.In GPCN, an individual consists of several trees which have the number P of executions.In previous work, the two kinds of modification, the conditional probability and the cross-cultural island model are employed.This paper proposes two methods: the new island model that combines the conditional probability with two islands in the cross-cultural island model and a method exchanges multiple trees in an individual in a suitable order.Experiments are conducted to show the performance in the garbage collection problem and the Santa Fe Trail problem.