Continuous probabilistic model building genetic network programming using reinforcement learning

Xianneng Li, Kotaro Hirasawa

研究成果: Article査読

4 被引用数 (Scopus)

抄録

Recently, a novel probabilistic model-building evolutionary algorithm (so called estimation of distribution algorithm, or EDA), named probabilistic model building genetic network programming (PMBGNP), has been proposed. PMBGNP uses graph structures for its individual representation, which shows higher expression ability than the classical EDAs. Hence, it extends EDAs to solve a range of problems, such as data mining and agent control. This paper is dedicated to propose a continuous version of PMBGNP for continuous optimization in agent control problems. Different from the other continuous EDAs, the proposed algorithm evolves the continuous variables by reinforcement learning (RL). We compare the performance with several state-of-the-art algorithms on a real mobile robot control problem. The results show that the proposed algorithm outperforms the others with statistically significant differences.

本文言語English
ページ(範囲)457-467
ページ数11
ジャーナルApplied Soft Computing Journal
27
DOI
出版ステータスPublished - 2015

ASJC Scopus subject areas

  • Software

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