Use of infeasible individuals in Probabilistic Model Building Genetic Network Programming

Xianneng Li, Shingo Mabu, Kotaro Hirasawa

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

15 Citations (Scopus)

Abstract

Classical EDAs generally use truncation selection to estimate the distribution of the feasible (good) individuals while ignoring the infeasible (bad) ones. However, various research in EAs reported that the infeasible individuals may affect and help the problem solving. This paper proposed a new method to use the infeasible individuals by studying the sub-structures rather than the entire individual structures to solve Reinforcement Learning (RL) problems, which generally factorize their entire solutions to the sequences of state-action pairs. This work was studied in a recent graph-based EDA named Probabilistic Model Building Genetic Network Programming (PMBGNP) which can solve RL problems successfully. The effectiveness of this work is verified in a RL problem, i.e., robot control, comparing with some other related work.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11
Pages601-608
Number of pages8
DOIs
Publication statusPublished - 2011
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin
Duration: 2011 Jul 122011 Jul 16

Other

Other13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
CityDublin
Period11/7/1211/7/16

Keywords

  • EDA
  • Infeasible individuals
  • Probabilistic Model Building Genetic Network Programming
  • Reinforcement learning

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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  • Cite this

    Li, X., Mabu, S., & Hirasawa, K. (2011). Use of infeasible individuals in Probabilistic Model Building Genetic Network Programming. In Genetic and Evolutionary Computation Conference, GECCO'11 (pp. 601-608) https://doi.org/10.1145/2001576.2001659