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A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning

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4 Author(s)
Xianneng Li ; Graduate School of Information, Production and Systems, Waseda University, Japan ; Bing Li ; Shingo Mabu ; Kotaro Hirasawa

This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.

Published in:

2011 IEEE Congress of Evolutionary Computation (CEC)

Date of Conference:

5-8 June 2011