The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization
We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA’s selection method is relatively unusual in comparison with current well known...
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 848 |
---|---|
container_issue | |
container_start_page | 839 |
container_title | |
container_volume | |
creator | Corne, David W. Knowles, Joshua D. Oates, Martin J. |
description | We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA’s selection method is relatively unusual in comparison with current well known multiobjective evolutionary algorithms, which tend to use counts based on the degree to which solutions dominate others in the population. The diversity maintenance method is similar to that used by certain other methods. The main attraction of PESA is the integration of selection and diversity maintenance, whereby essentially the same technique is used for both tasks. The resulting algorithm is simple to describe, with full pseudocode provided here and real code available from the authors. We compare PESA with two recent strong-performing MOEAs on some multiobjective test problems recently proposed by Deb. We find that PESA emerges as the best method overall on these problems. |
doi_str_mv | 10.1007/3-540-45356-3_82 |
format | Conference Proceeding |
fullrecord | <record><control><sourceid>pascalfrancis_sprin</sourceid><recordid>TN_cdi_pascalfrancis_primary_1384679</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1384679</sourcerecordid><originalsourceid>FETCH-LOGICAL-j2042-5a04896fdf75b92af696bc638c7bc4a44caead28f169d2d1490c05a5ee3516ec3</originalsourceid><addsrcrecordid>eNotkDtPwzAUhc1LopTujBlYXWxfP-KxVOUhtSoSZbYcx25T0iSKQyX49SSFuxzpfEd3-BC6o2RKCVEPgAUnmAsQEoNJ2RmaaJVCX546dY5GVFKKAbi-QDcnQImQ7BKNCBCGteJwjSYx7kl_wIRgeoRWm51P3mzruzpZVEdf1o3Hjzb6PHn3pXddUVfJrNzWbdHtDkmo22T1VfZtth_g0SfrpisOxY8dlrfoKtgy-sl_jtHH02Izf8HL9fPrfLbEe0Y4w8ISnmoZ8qBEppkNUsvMSUidyhy3nDvrbc7SQKXOWU65Jo4IK7wHQaV3MEb3f38bG50tQ2srV0TTtMXBtt-GQsql0v1s-jeLPam2vjVZXX9GQ4kZnBowvSVz8mcGp_ALaF9k5A</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization</title><source>Springer Books</source><creator>Corne, David W. ; Knowles, Joshua D. ; Oates, Martin J.</creator><contributor>Lutton, Evelyne ; Merelo, Juan Julian ; Schoenauer, Marc ; Deb, Kalyanmoy ; Yao, Xin ; Rudolph, Günther ; Schwefel, Hans-Paul</contributor><creatorcontrib>Corne, David W. ; Knowles, Joshua D. ; Oates, Martin J. ; Lutton, Evelyne ; Merelo, Juan Julian ; Schoenauer, Marc ; Deb, Kalyanmoy ; Yao, Xin ; Rudolph, Günther ; Schwefel, Hans-Paul</creatorcontrib><description>We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA’s selection method is relatively unusual in comparison with current well known multiobjective evolutionary algorithms, which tend to use counts based on the degree to which solutions dominate others in the population. The diversity maintenance method is similar to that used by certain other methods. The main attraction of PESA is the integration of selection and diversity maintenance, whereby essentially the same technique is used for both tasks. The resulting algorithm is simple to describe, with full pseudocode provided here and real code available from the authors. We compare PESA with two recent strong-performing MOEAs on some multiobjective test problems recently proposed by Deb. We find that PESA emerges as the best method overall on these problems.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540410562</identifier><identifier>ISBN: 9783540410560</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540453567</identifier><identifier>EISBN: 3540453563</identifier><identifier>DOI: 10.1007/3-540-45356-3_82</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Multiobjective Evolutionary Algorithm ; Multiobjective Genetic Algorithm ; Multiobjective Optimization ; Pareto Front ; Pareto Frontier ; Problem solving, game playing</subject><ispartof>Lecture notes in computer science, 2000, p.839-848</ispartof><rights>Springer-Verlag Berlin Heidelberg 2000</rights><rights>2000 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/3-540-45356-3_82$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/3-540-45356-3_82$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,4036,4037,27902,38232,41418,42487</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1384679$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Lutton, Evelyne</contributor><contributor>Merelo, Juan Julian</contributor><contributor>Schoenauer, Marc</contributor><contributor>Deb, Kalyanmoy</contributor><contributor>Yao, Xin</contributor><contributor>Rudolph, Günther</contributor><contributor>Schwefel, Hans-Paul</contributor><creatorcontrib>Corne, David W.</creatorcontrib><creatorcontrib>Knowles, Joshua D.</creatorcontrib><creatorcontrib>Oates, Martin J.</creatorcontrib><title>The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization</title><title>Lecture notes in computer science</title><description>We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA’s selection method is relatively unusual in comparison with current well known multiobjective evolutionary algorithms, which tend to use counts based on the degree to which solutions dominate others in the population. The diversity maintenance method is similar to that used by certain other methods. The main attraction of PESA is the integration of selection and diversity maintenance, whereby essentially the same technique is used for both tasks. The resulting algorithm is simple to describe, with full pseudocode provided here and real code available from the authors. We compare PESA with two recent strong-performing MOEAs on some multiobjective test problems recently proposed by Deb. We find that PESA emerges as the best method overall on these problems.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Multiobjective Evolutionary Algorithm</subject><subject>Multiobjective Genetic Algorithm</subject><subject>Multiobjective Optimization</subject><subject>Pareto Front</subject><subject>Pareto Frontier</subject><subject>Problem solving, game playing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540410562</isbn><isbn>9783540410560</isbn><isbn>9783540453567</isbn><isbn>3540453563</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2000</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkDtPwzAUhc1LopTujBlYXWxfP-KxVOUhtSoSZbYcx25T0iSKQyX49SSFuxzpfEd3-BC6o2RKCVEPgAUnmAsQEoNJ2RmaaJVCX546dY5GVFKKAbi-QDcnQImQ7BKNCBCGteJwjSYx7kl_wIRgeoRWm51P3mzruzpZVEdf1o3Hjzb6PHn3pXddUVfJrNzWbdHtDkmo22T1VfZtth_g0SfrpisOxY8dlrfoKtgy-sl_jtHH02Izf8HL9fPrfLbEe0Y4w8ISnmoZ8qBEppkNUsvMSUidyhy3nDvrbc7SQKXOWU65Jo4IK7wHQaV3MEb3f38bG50tQ2srV0TTtMXBtt-GQsql0v1s-jeLPam2vjVZXX9GQ4kZnBowvSVz8mcGp_ALaF9k5A</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Corne, David W.</creator><creator>Knowles, Joshua D.</creator><creator>Oates, Martin J.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2000</creationdate><title>The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization</title><author>Corne, David W. ; Knowles, Joshua D. ; Oates, Martin J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-j2042-5a04896fdf75b92af696bc638c7bc4a44caead28f169d2d1490c05a5ee3516ec3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Multiobjective Evolutionary Algorithm</topic><topic>Multiobjective Genetic Algorithm</topic><topic>Multiobjective Optimization</topic><topic>Pareto Front</topic><topic>Pareto Frontier</topic><topic>Problem solving, game playing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Corne, David W.</creatorcontrib><creatorcontrib>Knowles, Joshua D.</creatorcontrib><creatorcontrib>Oates, Martin J.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Corne, David W.</au><au>Knowles, Joshua D.</au><au>Oates, Martin J.</au><au>Lutton, Evelyne</au><au>Merelo, Juan Julian</au><au>Schoenauer, Marc</au><au>Deb, Kalyanmoy</au><au>Yao, Xin</au><au>Rudolph, Günther</au><au>Schwefel, Hans-Paul</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization</atitle><btitle>Lecture notes in computer science</btitle><date>2000</date><risdate>2000</risdate><spage>839</spage><epage>848</epage><pages>839-848</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540410562</isbn><isbn>9783540410560</isbn><eisbn>9783540453567</eisbn><eisbn>3540453563</eisbn><abstract>We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA’s selection method is relatively unusual in comparison with current well known multiobjective evolutionary algorithms, which tend to use counts based on the degree to which solutions dominate others in the population. The diversity maintenance method is similar to that used by certain other methods. The main attraction of PESA is the integration of selection and diversity maintenance, whereby essentially the same technique is used for both tasks. The resulting algorithm is simple to describe, with full pseudocode provided here and real code available from the authors. We compare PESA with two recent strong-performing MOEAs on some multiobjective test problems recently proposed by Deb. We find that PESA emerges as the best method overall on these problems.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/3-540-45356-3_82</doi><tpages>10</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0302-9743 |
ispartof | Lecture notes in computer science, 2000, p.839-848 |
issn | 0302-9743 1611-3349 |
language | eng |
recordid | cdi_pascalfrancis_primary_1384679 |
source | Springer Books |
subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Multiobjective Evolutionary Algorithm Multiobjective Genetic Algorithm Multiobjective Optimization Pareto Front Pareto Frontier Problem solving, game playing |
title | The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-19T07%3A14%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=The%20Pareto%20Envelope-Based%20Selection%20Algorithm%20for%20Multiobjective%20Optimization&rft.btitle=Lecture%20notes%20in%20computer%20science&rft.au=Corne,%20David%20W.&rft.date=2000&rft.spage=839&rft.epage=848&rft.pages=839-848&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=3540410562&rft.isbn_list=9783540410560&rft_id=info:doi/10.1007/3-540-45356-3_82&rft_dat=%3Cpascalfrancis_sprin%3E1384679%3C/pascalfrancis_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=9783540453567&rft.eisbn_list=3540453563&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |