書誌事項
- タイトル別名
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- Constraint-Handling Method for Function Optimization: Pareto Descent Repair Operator
- カンスウ サイテキカ ノ タメノ セイヤク タイショホウ パレート コウカ シュウセイ オペレータ
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抄録
Function optimization underlies many real-world problems and hence is an important research subject. Most of the existing optimization methods were developed to solve primarily unconstrained problems. Since real-world problems are often constrained, appropriate handling of constraints is necessary in order to use the optimization methods. In particular, the performances of some methods such as Genetic Algorithms (GA) can be substantially undermined by ineffective constraint handling. Despite much effort devoted to the studies of constraint-handling methods, it has been reported that each of them has certain limitations. Hence, further studies for designing more effective constraint-handling methods are needed. <BR> For this reason, we investigated the guidelines for a method to effectively handle constraints. The guidelines are that the method 1) takes the approach of repair operators, 2) monotonically decreases both the number of violated constraints and constraint violations, and 3) searches over the boundaries of violated constraints. Based on these guidelines, we designed a new constraint-handling method Pareto Descent Repair operator (PDR) in which ideas derived from multi-objective local search and gradient projection method are incorporated. Experiments comparing GA that use PDR and some of the existing constraint-handling methods confirmed the effectiveness of PDR.
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 22 (4), 364-374, 2007
一般社団法人 人工知能学会
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詳細情報 詳細情報について
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- CRID
- 1390282680083288576
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- NII論文ID
- 10022007740
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- NII書誌ID
- AA11579226
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- ISSN
- 13468030
- 13460714
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- NDL書誌ID
- 9604086
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可
