A novel graph-based estimation of the distribution algorithm and its extension using reinforcement learning

Xianneng Li, Shingo Mabu, Kotaro Hirasawa

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

In recent years, numerous studies have drawn the success of estimation of distribution algorithms (EDAs) to avoid the frequent breakage of building blocks of the conventional stochastic genetic operators-based evolutionary algorithms (EAs). In this paper, a novel graph-based EDA called probabilistic model building genetic network programming (PMBGNP) is proposed. Using the distinguished graph (network) structure of a graph-based EA called genetic network programming (GNP), PMBGNP ensures higher expression ability than the conventional EDAs to solve some specific problems. Furthermore, an extended algorithm called reinforced PMBGNP is proposed to combine PMBGNP and reinforcement learning to enhance the performance in terms of fitness values, search speed, and reliability. The proposed algorithms are applied to solve the problems of controlling the agents' behavior. Two problems are selected to demonstrate the effectiveness of the proposed algorithms, including the benchmark one, i.e., the Tileworld system, and a real mobile robot control.

Original languageEnglish
Article number6408015
Pages (from-to)98-113
Number of pages16
JournalIEEE Transactions on Evolutionary Computation
Volume18
Issue number1
DOIs
Publication statusPublished - 2014 Feb

Keywords

  • Agent control
  • estimation of distribution algorithm (EDA)
  • genetic network programming (GNP)
  • graph structure
  • reinforcement learning (RL)

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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