Analysis of The Behavior of MGG and JGG As A Selection Model for Real-coded Genetic Algorithms

• 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

• 実数値ＧＡにおける生存選択モデルとしてのＭＧＧとＪＧＧの挙動解析

Abstract

In this paper, we focus on analyzing the behavior of the selection models for real-coded genetic algorithms. Recent studies show that Just Generation Gap (JGG) selection model outperforms Minimal Generation Gap (MGG) model when a multi-parental crossover operator based on the hypothesis of the preservation of the statistics of parents is used. However, the validation of JGG selection model is not done yet. To validate the selection method of JGG, we analyze the differences of the behavior of JGG selection model and that of MGG selection model.

Journal

• Transactions of the Japanese Society for Artificial Intelligence

  Transactions of the Japanese Society for Artificial Intelligence 25 (2), 281-289, 2010

  The Japanese Society for Artificial Intelligence

Keywords

• function optimization
• minimal generation gap
• just generation gap

Details

• CRID: 1390001205109072384
• NII Article ID: 130000259120
• DOI: 10.1527/tjsai.25.281
• ISSN: 13468030
• ISSN: 13460714
• Web Site: http://www.jstage.jst.go.jp/article/tjsai/25/2/25_2_281/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed