| ○種別 (必須): | □ | 学術論文 (審査論文)
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| ○言語 (必須): | □ | 日本語
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| ○カテゴリ (推奨): | □ | 研究
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| ○著者 (必須): | 1. | 永田 裕一 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.知能情報コース.情報工学講座])
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| ○題名 (必須): | □ | (英) New Approach of a Genetic Algorithm for TSP Using the Evaluation Function Considering Local Diversity Loss (日) 局所的多様性の損失を考慮した評価関数を用いたGAのTSPへの適用,
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| ○要約 (任意): | □ | (英) The edge assembly crossover (EAX) is considered the best available crossover for traveling salesman problems (TSPs). In this paper, a modified EAX algorithm is proposed. The key idea is to maintain population diversity by eliminating any exchanges of edges by the crossover that does not contribute to an improved evaluation value. For this, a new evaluation function is designed considering local diversity loss of the population. The proposed method is applied to several benchmark instances with up to 4461 cities. Experimental results show that the proposed method works better than other genetic algorithms using other improvements of the EAX. The proposed method can reach optimal solutions for most benchmark instances with up to 2392 cities with probabilities higher than 90%. For an instance called fnl4461, this method can reach an optimal solution with probability 60% when the population size is set to 300 -- an extremely small population compared to that needed in previous studies. (日) The edge assembly crossover (EAX) is considered the best available crossover for traveling salesman problems (TSPs). In this paper, a modified EAX algorithm is proposed. The key idea is to maintain population diversity by eliminating any exchanges of edges by the crossover that does not contribute to an improved evaluation value. For this, a new evaluation function is designed considering local diversity loss of the population. The proposed method is applied to several benchmark instances with up to 4461 cities. Experimental results show that the proposed method works better than other genetic algorithms using other improvements of the EAX. The proposed method can reach optimal solutions for most benchmark instances with up to 2392 cities with probabilities higher than 90%. For an instance called fnl4461, this method can reach an optimal solution with probability 60% when the population size is set to 300 -- an extremely small population compared to that needed in previous studies.
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| ○キーワード (推奨): | 1. | (英) genetic algorithm (日) (読)
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| 2. | (英) TSP (日) EAX (読)
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| 3. | (英) EAX (日) local diversity loss (読)
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| 4. | (英) local diversity loss (日) evaluation function (読)
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| 5. | (英) evaluation function (日) (読)
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| ○発行所 (推奨): | □ | (英) The Japanese Society for Artificial Intelligence (日) 人工知能学会 (読)
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| ○誌名 (必須): | □ | 人工知能学会論文誌 ([社団法人 人工知能学会])
(pISSN: 1346-0714, eISSN: 1346-8030)
| ○ISSN (任意): | □ | 1346-0714
ISSN: 1346-0714
(pISSN: 1346-0714, eISSN: 1346-8030) Title: Transactions of the Japanese Society for Artificial Intelligence = Jinko Chino Gakkai ronbunshiTitle(ISO): Trans Jpn Soc Artif IntellSupplier: 一般社団法人 人工知能学会Publisher: Japanese Society for Artificial Intelligence (NLM Catalog)
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| ○巻 (必須): | □ | 21
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| ○号 (必須): | □ | 2
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| ○頁 (必須): | □ | 195 204
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| ○年月日 (必須): | □ | 西暦 2006年 2月 初日 (平成 18年 2月 初日)
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| ○URL (任意): | □ | http://ci.nii.ac.jp/naid/10022006211/
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| ○DOI (任意): | □ | 10.1527/tjsai.21.195 (→Scopusで検索)
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| ○PMID (任意): |
| ○CRID (任意): | □ | 1390282680083497216
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| ○NAID : | □ | 10022006211
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| ○Scopus (任意): | □ | 2-s2.0-32044441664
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