遺伝的アルゴリズムにおける世代交代モデルの提案と評価

• 佐藤 浩

  東京工業大学大学院総合理工学研究科 大阪府立大学総合科学部

• 小野 功

  東京工業大学大学院総合理工学研究科

• 小林 重信

  東京工業大学大学院総合理工学研究科

書誌事項

タイトル別名

• A New Generation Alternation Model of Genetic Algorithms and Its Assessment
• イデンテキ アルゴリズム ニ オケル セダイ コウタイ モデル ノ テイアン

抄録

When Genetic Algorithms (GAs) are applied to optimization problems, characteristic preserving in designing coding/crossover and diversity maintaining in designing generation alternation are important. Generation alternation models are independent of problems, while coding/crossover depends on problems. We discuss generation alternation models in this paper. Simple GA is one of the well-known generation alternation models, however it has two problems. One is early convergence in the first stage of search and the other is evolutionary stagnation in the last stage of it. Many improvements and new models have been presented to overcome the above problems. In this paper, we propose a new generation alternation model called minimal generation gap (MGG) which has all advantages of conventional models. As generation alternation models use only information of fitness, alternation of generations can be regarded as a transformation of fitness distributions. We propose a new method of assessing generation alternation models. We measure the ability of avoiding the early convergence and suppressing the evolutionary stagnation by the dynamics of the best value and variance of fitness distributions. From the results of some experiments, we found that MGG is the most desirable model which can avoid the early convergence and suppress the evolutionary stagnation. We also show the efficiency of MGG by applying it to benchmarks in different two domains: function optimization and traveling salesman problems. In the both domains, MGG showed higher performance than the other conventional models especially under small population size.

収録刊行物

• 人工知能

  人工知能 12 (5), 734-744, 1997-09-01

  一般社団法人 人工知能学会

キーワード

• genetic algorithms
• generation alternation models
• early convergence
• evolutionary stagnation
• fitness distribution

詳細情報

• CRID: 1390285697602315904
• NII論文ID: 110002806861
• NII論文ID: 10012130367
• NII書誌ID: AN10067140
• ISSN: 09128085
• ISSN: 24358614
• ISSN: 21882266
• DOI: 10.11517/jjsai.12.5_734
• NDL書誌ID: 4283137
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I4283137
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • NDL
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可