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

Xianneng Li, Bing Li, Shingo Mabu, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2011 IEEE Congress of Evolutionary Computation, CEC 2011
Pages37-44
Number of pages8
DOIs
Publication statusPublished - 2011
Event2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA
Duration: 2011 Jun 52011 Jun 8

Other

Other2011 IEEE Congress of Evolutionary Computation, CEC 2011
CityNew Orleans, LA
Period11/6/511/6/8

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Li, X., Li, B., Mabu, S., & Hirasawa, K. (2011). A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning. In 2011 IEEE Congress of Evolutionary Computation, CEC 2011 (pp. 37-44). [5949595] https://doi.org/10.1109/CEC.2011.5949595