Guiding Single-Objective Optimization Using Multi-objective Methods
This paper investigates the possibility of using multi-objective methods to guide the search when solving single-objective optimization problems with genetic algorithms.Using the job shop scheduling problem as an example,experiments demonstrate that by using helper-objectives (additional objectives...
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| creator | Jensen, Mikkel T. |
| description | This paper investigates the possibility of using multi-objective methods to guide the search when solving single-objective optimization problems with genetic algorithms.Using the job shop scheduling problem as an example,experiments demonstrate that by using helper-objectives (additional objectives guiding the search),the average performance of a standard GA can be significantly improved.The helper-objectives guide the search towards solutions containing good building blocks and helps the algorithm avoid local optima.The experiments reveal that the approach only works if the number of helper-objectives used simultaneously is low.However,a high number of helper-objectives can be used in the same run by changing the helper-objectives dynamically. |
| doi_str_mv | 10.1007/3-540-36605-9_25 |
| format | Book Chapter |
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Romero ; Corne, David W ; Meyer, Jean-Arcady ; Gottlieb, Jens ; Johnson, Colin G ; Guillot, Agnes ; Hart, Emma ; Raidl, Günther ; Marchiori, Elena ; Cagnoni, Stefano ; Middendorf, Martin ; Cardalda, Juan J. Romero ; Corne, David W. ; Meyer, Jean-Arcady ; Guillot, Agnès ; Johnson, Colin G. ; Marchiori, Elena ; Gottlieb, Jens ; Cagnoni, Stefano ; Raidl, Günther R. ; Hart, Emma</contributor><creatorcontrib>Jensen, Mikkel T. ; Cardalda, Juan J. Romero ; Corne, David W ; Meyer, Jean-Arcady ; Gottlieb, Jens ; Johnson, Colin G ; Guillot, Agnes ; Hart, Emma ; Raidl, Günther ; Marchiori, Elena ; Cagnoni, Stefano ; Middendorf, Martin ; Cardalda, Juan J. Romero ; Corne, David W. ; Meyer, Jean-Arcady ; Guillot, Agnès ; Johnson, Colin G. ; Marchiori, Elena ; Gottlieb, Jens ; Cagnoni, Stefano ; Raidl, Günther R. ; Hart, Emma</creatorcontrib><description>This paper investigates the possibility of using multi-objective methods to guide the search when solving single-objective optimization problems with genetic algorithms.Using the job shop scheduling problem as an example,experiments demonstrate that by using helper-objectives (additional objectives guiding the search),the average performance of a standard GA can be significantly improved.The helper-objectives guide the search towards solutions containing good building blocks and helps the algorithm avoid local optima.The experiments reveal that the approach only works if the number of helper-objectives used simultaneously is low.However,a high number of helper-objectives can be used in the same run by changing the helper-objectives dynamically.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540009760</identifier><identifier>ISBN: 9783540009764</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540366058</identifier><identifier>EISBN: 3540366059</identifier><identifier>DOI: 10.1007/3-540-36605-9_25</identifier><identifier>OCLC: 935291330</identifier><identifier>LCCallNum: QA75.5-76.95</identifier><language>eng</language><publisher>Germany: Springer Berlin / Heidelberg</publisher><subject>Algorithmics. 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Management science ; Problem Instance ; Scheduling, sequencing ; Theoretical computing ; Traditional Algorithm ; Travelling Salesperson Problem</subject><ispartof>Applications of Evolutionary Computing, 2003, Vol.2611, p.268-279</ispartof><rights>Springer-Verlag Berlin Heidelberg 2003</rights><rights>2003 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c315t-f95de66c751de793cfd93cf89c1b14a7f98a4c1aaec76a560ba492f8933b33713</citedby><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttps://ebookcentral.proquest.com/covers/3073002-l.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/3-540-36605-9_25$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/3-540-36605-9_25$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,779,780,784,789,790,793,4050,4051,27925,38255,41442,42511</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14841233$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Cardalda, Juan J. Romero</contributor><contributor>Corne, David W</contributor><contributor>Meyer, Jean-Arcady</contributor><contributor>Gottlieb, Jens</contributor><contributor>Johnson, Colin G</contributor><contributor>Guillot, Agnes</contributor><contributor>Hart, Emma</contributor><contributor>Raidl, Günther</contributor><contributor>Marchiori, Elena</contributor><contributor>Cagnoni, Stefano</contributor><contributor>Middendorf, Martin</contributor><contributor>Cardalda, Juan J. Romero</contributor><contributor>Corne, David W.</contributor><contributor>Meyer, Jean-Arcady</contributor><contributor>Guillot, Agnès</contributor><contributor>Johnson, Colin G.</contributor><contributor>Marchiori, Elena</contributor><contributor>Gottlieb, Jens</contributor><contributor>Cagnoni, Stefano</contributor><contributor>Raidl, Günther R.</contributor><contributor>Hart, Emma</contributor><creatorcontrib>Jensen, Mikkel T.</creatorcontrib><title>Guiding Single-Objective Optimization Using Multi-objective Methods</title><title>Applications of Evolutionary Computing</title><description>This paper investigates the possibility of using multi-objective methods to guide the search when solving single-objective optimization problems with genetic algorithms.Using the job shop scheduling problem as an example,experiments demonstrate that by using helper-objectives (additional objectives guiding the search),the average performance of a standard GA can be significantly improved.The helper-objectives guide the search towards solutions containing good building blocks and helps the algorithm avoid local optima.The experiments reveal that the approach only works if the number of helper-objectives used simultaneously is low.However,a high number of helper-objectives can be used in the same run by changing the helper-objectives dynamically.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Good Building Block</subject><subject>Multiobjective Optimization</subject><subject>Operational research and scientific management</subject><subject>Operational research. 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Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Good Building Block</topic><topic>Multiobjective Optimization</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Problem Instance</topic><topic>Scheduling, sequencing</topic><topic>Theoretical computing</topic><topic>Traditional Algorithm</topic><topic>Travelling Salesperson Problem</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jensen, Mikkel T.</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jensen, Mikkel T.</au><au>Cardalda, Juan J. Romero</au><au>Corne, David W</au><au>Meyer, Jean-Arcady</au><au>Gottlieb, Jens</au><au>Johnson, Colin G</au><au>Guillot, Agnes</au><au>Hart, Emma</au><au>Raidl, Günther</au><au>Marchiori, Elena</au><au>Cagnoni, Stefano</au><au>Middendorf, Martin</au><au>Cardalda, Juan J. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Applications of Evolutionary Computing, 2003, Vol.2611, p.268-279 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_14841233 |
| source | Springer Books |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Computer science control theory systems Exact sciences and technology Good Building Block Multiobjective Optimization Operational research and scientific management Operational research. Management science Problem Instance Scheduling, sequencing Theoretical computing Traditional Algorithm Travelling Salesperson Problem |
| title | Guiding Single-Objective Optimization Using Multi-objective Methods |
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