Useful Infeasible Solutions in Engineering Optimization with Evolutionary Algorithms
We propose an evolutionary-based approach to solve engineering design problems without using penalty functions. The aim is to identify and maintain infeasible solutions close to the feasible region located in promising areas. In this way, using the genetic operators, more solutions will be generated...
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| creator | Mezura-Montes, Efrén Coello, Carlos A. Coello |
| description | We propose an evolutionary-based approach to solve engineering design problems without using penalty functions. The aim is to identify and maintain infeasible solutions close to the feasible region located in promising areas. In this way, using the genetic operators, more solutions will be generated inside the feasible region and also near its boundaries. As a result, the feasible region will be sampled well-enough as to reach better feasible solutions. The proposed approach, which is simple to implement, is tested with respect to typical penalty function techniques as well as against state-of-the-art approaches using four mechanical design problems. The results obtained are discussed and some conclusions are provided. |
| doi_str_mv | 10.1007/11579427_66 |
| format | Conference Proceeding |
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Coello</creator><contributor>Terashima-Marín, Hugo ; de Albornoz, Álvaro ; Gelbukh, Alexander</contributor><creatorcontrib>Mezura-Montes, Efrén ; Coello, Carlos A. Coello ; Terashima-Marín, Hugo ; de Albornoz, Álvaro ; Gelbukh, Alexander</creatorcontrib><description>We propose an evolutionary-based approach to solve engineering design problems without using penalty functions. The aim is to identify and maintain infeasible solutions close to the feasible region located in promising areas. In this way, using the genetic operators, more solutions will be generated inside the feasible region and also near its boundaries. As a result, the feasible region will be sampled well-enough as to reach better feasible solutions. The proposed approach, which is simple to implement, is tested with respect to typical penalty function techniques as well as against state-of-the-art approaches using four mechanical design problems. 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Coello</creatorcontrib><title>Useful Infeasible Solutions in Engineering Optimization with Evolutionary Algorithms</title><title>Lecture notes in computer science</title><description>We propose an evolutionary-based approach to solve engineering design problems without using penalty functions. The aim is to identify and maintain infeasible solutions close to the feasible region located in promising areas. In this way, using the genetic operators, more solutions will be generated inside the feasible region and also near its boundaries. As a result, the feasible region will be sampled well-enough as to reach better feasible solutions. The proposed approach, which is simple to implement, is tested with respect to typical penalty function techniques as well as against state-of-the-art approaches using four mechanical design problems. The results obtained are discussed and some conclusions are provided.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Constraint Violation</subject><subject>Evolutionary Algorithm</subject><subject>Exact sciences and technology</subject><subject>Feasible Region</subject><subject>Feasible Solution</subject><subject>Penalty Function</subject><subject>Problem solving, game playing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540298960</isbn><isbn>3540298967</isbn><isbn>3540316531</isbn><isbn>9783540316534</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkD9PwzAQxc0_iVA68QW8MDAEfLZjx2NVFahUqQPtbDmxUwypE8UpCD49idqBW06699PpvYfQHZBHIEQ-AWRScSq1EGfohmWcMBAZg3OUgABIGePqAk2VzEeNqlwJcokSwghNleTsGk1j_CDDMMgVlQnabKOrDjVehsqZ6Iva4bemPvS-CRH7gBdh54NznQ87vG57v_e_ZhTxt-_f8eLrxJruB8_qXdMN1328RVeVqaObnvYEbZ8Xm_lrulq_LOezVVpSAX0KVipHnRLOSK44AWpzSxwprLXEOjY4KgpKqIOqLLJKMpvJXBpHKSulsopN0P3xb2tiaeqqM6H0Ubed3w-GNEg-tjJyD0cutmMQ1-miaT6jBqLHWvW_WtkfuDZmlQ</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Mezura-Montes, Efrén</creator><creator>Coello, Carlos A. 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Coello</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c261t-1d79e2e96ea7494012d8d0e0bddd0de3feabb202e1fcb5f73d5787ae223c79d93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Constraint Violation</topic><topic>Evolutionary Algorithm</topic><topic>Exact sciences and technology</topic><topic>Feasible Region</topic><topic>Feasible Solution</topic><topic>Penalty Function</topic><topic>Problem solving, game playing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mezura-Montes, Efrén</creatorcontrib><creatorcontrib>Coello, Carlos A. 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The aim is to identify and maintain infeasible solutions close to the feasible region located in promising areas. In this way, using the genetic operators, more solutions will be generated inside the feasible region and also near its boundaries. As a result, the feasible region will be sampled well-enough as to reach better feasible solutions. The proposed approach, which is simple to implement, is tested with respect to typical penalty function techniques as well as against state-of-the-art approaches using four mechanical design problems. The results obtained are discussed and some conclusions are provided.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11579427_66</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2005, p.652-662 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17416119 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Constraint Violation Evolutionary Algorithm Exact sciences and technology Feasible Region Feasible Solution Penalty Function Problem solving, game playing |
| title | Useful Infeasible Solutions in Engineering Optimization with Evolutionary Algorithms |
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