Efficient Real-Coded Genetic Algorithms with Flexible-Step Crossover

• Mutoh Atsuko

  Nagoya Institute of Technology

• Tanahashi Fumiki

  Toyota Motor Corporation

• Kato Shohei

  Nagoya Institute of Technology

• Itoh Hidenori

  Nagoya Institute of Technology

Abstract

Real-coded genetic algorithms (GAs) are effective methods for function optimization. Generally speaking, the major crossover methods used in real-coded GAs require a large execution time for calculating the fitness of many children at each crossover. Thus, a new crossover method is needed for searching such a large search space efficiently. A novel crossover method that generates children stepwise is proposed and applied to the conventional generation-alternation model. In experiments based on standard test functions and actual problems, the proposed model found an optimal solution 30-40% faster than did the conventional model.

Journal

• IEEJ Transactions on Electronics, Information and Systems

  IEEJ Transactions on Electronics, Information and Systems 126 (5), 654-660, 2006

  The Institute of Electrical Engineers of Japan

Keywords

• real-coded genetic algorithms
• SPX
• MGG
• function optimization
• motor optimization

Details

• CRID: 1390282679582058496
• NII Article ID: 10018111485
• NII Book ID: AN10065950
• DOI: 10.1541/ieejeiss.126.654
• ISSN: 13488155
• ISSN: 03854221
• NDL BIB ID: 7947325
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7947325
• Web Site: http://www.jstage.jst.go.jp/article/ieejeiss/126/5/126_5_654/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed