Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learning

Yang Chen, Jinglu Hu, Kotaro Hirasawa, Songnian Yu

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

3 Citations (Scopus)

Abstract

Recently, an improved genetic algorithm with a reserve selection mechanism (GARS) has been proposed to prevent premature convergence, where a parameter called reserve size plays an important role in optimization performance. In this paper, we propose an approach to the learning of an optimal reserve size in GARS based on the technique of reinforcement learning, where the learning model and algorithm are presented respectively. The experimental results demonstrate the effectiveness of learning algorithm in discovering the optimal reserve size accurately and efficiently.

Original languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Pages1341-1347
Number of pages7
DOIs
Publication statusPublished - 2007 Dec 1
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: 2007 Sep 172007 Sep 20

Publication series

NameProceedings of the SICE Annual Conference

Conference

ConferenceSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CountryJapan
CityTakamatsu
Period07/9/1707/9/20

Keywords

  • Genetic algorithms
  • Global optimization
  • Population diversity
  • Premature convergence
  • Reinforcement learning
  • Reserve selection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Chen, Y., Hu, J., Hirasawa, K., & Yu, S. (2007). Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learning. In SICE Annual Conference, SICE 2007 (pp. 1341-1347). [4421191] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2007.4421191