Proposal and Evaluation of Adaptive Real-coded Crossover AREX

• Akimoto Youhei

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

• Nagata Yuichi

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

• Sakuma Jun

  Graduate School of Systems and Information Engineering, University of Tsukuba

• Ono Isao

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

• Kobayashi Shigenobu

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

Bibliographic Information

Other Title

• 適応的実数値交叉　ＡＲＥＸ　の提案と評価

Abstract

Since once premature convergence happens evolutionary algorithms for function optimization can no longer explore areas of the search space and fail to find the optimum, it is required to handle the notorious drawback. This paper proposes two novel approaches to overcome premature convergence of real-coded genetic algorithms (RCGAs). The first idea is to control the sampling region of crossover by adaptation of expansion rate. The second idea is to cause the acceleration of the movement of population by descending the mean of crossover. Finally, we propose a crossover that combines the adaptation of expansion rate technique and the crossover mean descent technique, called AREX (adaptive real-coded ensemble crossover). The performance of the real-coded GA using AREX is evaluated on several benchmark functions including functions whose landscape forms ridge structure or multi-peak structure, both of which are likely to lead to the miserable convergence phenomenon. The experimental results show not only that the proposed method can locate the global optima of functions on which it is difficult for the existing GAs to discover it but also that our approach outperforms the existing one in number of function evaluations on all functions. Our approach enlarges the classes of functions that real-coded GAs can solve.

Journal

• Transactions of the Japanese Society for Artificial Intelligence

  Transactions of the Japanese Society for Artificial Intelligence 24 (6), 446-458, 2009

  The Japanese Society for Artificial Intelligence

Keywords

• function optimization
• adaptation of expansion rate
• crossover mean descent
• adaptive real-coded ensemble crossover

Details

• CRID: 1390001205108974592
• NII Article ID: 130000137880
• DOI: 10.1527/tjsai.24.446
• ISSN: 13468030
• ISSN: 13460714
• Web Site: http://www.jstage.jst.go.jp/article/tjsai/24/6/24_6_446/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed