Obtaining Repetitive Actions for Genetic Programming with Multiple Trees
Takashi Ito, Keníchi Takahashi, Michimasa Inaba · Procedia Computer Science · 2016
This paper proposes a method to improve genetic programming with multiple trees (GP CN ). An individual in GP CN comprises multiple trees, and each tree has a number P that indicates the number of repetitive actions based on the tree. In previous work, a method for updating the number P has been proposed to obtain P suitable to the tree in evolution. However, in the method efficiency becomes worse as the range of P becomes wider. In order to solve the problem, in this study, two methods are proposed: inheriting the number P of a tree from an excellent individual and using mutation for preventing the number P from being into a local optimum. Additionally, a method to eliminate trees consisting of a single terminal node is proposed.