Obtaining Repetitive Actions for Genetic Programming with Multiple Trees

https://doi.org/10.1016/j.procs.2016.08.111Get rights and content
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Abstract

This paper proposes a method to improve genetic programming with multiple trees (GPCN). An individual in GPCN 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.

Keywords

autonomous agent
garbage collection problem
genetic programming
evolutionary learning
multiple trees.

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