Decision making in a hybrid genetic algorithm
There are several issues that need to be taken into consideration when designing a hybrid problem solver. The paper focuses on one of them-decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 125 |
|---|---|
| container_issue | |
| container_start_page | 121 |
| container_title | |
| container_volume | |
| creator | Lobo, F.G. Goldberg, D.E. |
| description | There are several issues that need to be taken into consideration when designing a hybrid problem solver. The paper focuses on one of them-decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hill climber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC are needed to solve the problem efficiently. |
| doi_str_mv | 10.1109/ICEC.1997.592281 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_592281</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>592281</ieee_id><sourcerecordid>592281</sourcerecordid><originalsourceid>FETCH-LOGICAL-i104t-3227cda9d0d10d0b76fac53622143e1e4bf5df1e3b8dee2bcf861eae1f9ba1be3</originalsourceid><addsrcrecordid>eNotj8tKw0AUQAdEqNbui6v5gcR755FklhKrLRTc6LrM4056tUklyaZ_r1DP5uwOHCHWCCUiuKddu2lLdK4urVOqwRtxD3UDWjvj7EKspukL_jDGauvuRPFCkSc-D7L33zx0kgfp5fESRk6yo4FmjtKfuvPI87F_ELfZnyZa_XspPl83H-222L-_7drnfcEIZi60UnVM3iVICAlCXWUfra6UQqMJyYRsU0bSoUlEKsTcVEieMLvgMZBeisdrl4no8DNy78fL4TqkfwFuBUE4</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Decision making in a hybrid genetic algorithm</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Lobo, F.G. ; Goldberg, D.E.</creator><creatorcontrib>Lobo, F.G. ; Goldberg, D.E.</creatorcontrib><description>There are several issues that need to be taken into consideration when designing a hybrid problem solver. The paper focuses on one of them-decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hill climber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC are needed to solve the problem efficiently.</description><identifier>ISBN: 0780339495</identifier><identifier>ISBN: 9780780339491</identifier><identifier>DOI: 10.1109/ICEC.1997.592281</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Decision making ; Diversity reception ; Expert systems ; Genetic algorithms ; Jet engines ; Maintenance engineering ; Mathematical analysis ; Testing ; Turbines</subject><ispartof>Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97), 1997, p.121-125</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/592281$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,4048,4049,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/592281$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lobo, F.G.</creatorcontrib><creatorcontrib>Goldberg, D.E.</creatorcontrib><title>Decision making in a hybrid genetic algorithm</title><title>Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)</title><addtitle>ICEC</addtitle><description>There are several issues that need to be taken into consideration when designing a hybrid problem solver. The paper focuses on one of them-decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hill climber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC are needed to solve the problem efficiently.</description><subject>Algorithm design and analysis</subject><subject>Decision making</subject><subject>Diversity reception</subject><subject>Expert systems</subject><subject>Genetic algorithms</subject><subject>Jet engines</subject><subject>Maintenance engineering</subject><subject>Mathematical analysis</subject><subject>Testing</subject><subject>Turbines</subject><isbn>0780339495</isbn><isbn>9780780339491</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1997</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tKw0AUQAdEqNbui6v5gcR755FklhKrLRTc6LrM4056tUklyaZ_r1DP5uwOHCHWCCUiuKddu2lLdK4urVOqwRtxD3UDWjvj7EKspukL_jDGauvuRPFCkSc-D7L33zx0kgfp5fESRk6yo4FmjtKfuvPI87F_ELfZnyZa_XspPl83H-222L-_7drnfcEIZi60UnVM3iVICAlCXWUfra6UQqMJyYRsU0bSoUlEKsTcVEieMLvgMZBeisdrl4no8DNy78fL4TqkfwFuBUE4</recordid><startdate>1997</startdate><enddate>1997</enddate><creator>Lobo, F.G.</creator><creator>Goldberg, D.E.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1997</creationdate><title>Decision making in a hybrid genetic algorithm</title><author>Lobo, F.G. ; Goldberg, D.E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-3227cda9d0d10d0b76fac53622143e1e4bf5df1e3b8dee2bcf861eae1f9ba1be3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Algorithm design and analysis</topic><topic>Decision making</topic><topic>Diversity reception</topic><topic>Expert systems</topic><topic>Genetic algorithms</topic><topic>Jet engines</topic><topic>Maintenance engineering</topic><topic>Mathematical analysis</topic><topic>Testing</topic><topic>Turbines</topic><toplevel>online_resources</toplevel><creatorcontrib>Lobo, F.G.</creatorcontrib><creatorcontrib>Goldberg, D.E.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lobo, F.G.</au><au>Goldberg, D.E.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Decision making in a hybrid genetic algorithm</atitle><btitle>Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)</btitle><stitle>ICEC</stitle><date>1997</date><risdate>1997</risdate><spage>121</spage><epage>125</epage><pages>121-125</pages><isbn>0780339495</isbn><isbn>9780780339491</isbn><abstract>There are several issues that need to be taken into consideration when designing a hybrid problem solver. The paper focuses on one of them-decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hill climber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC are needed to solve the problem efficiently.</abstract><pub>IEEE</pub><doi>10.1109/ICEC.1997.592281</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 0780339495 |
| ispartof | Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97), 1997, p.121-125 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_592281 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Decision making Diversity reception Expert systems Genetic algorithms Jet engines Maintenance engineering Mathematical analysis Testing Turbines |
| title | Decision making in a hybrid genetic algorithm |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T02%3A22%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Decision%20making%20in%20a%20hybrid%20genetic%20algorithm&rft.btitle=Proceedings%20of%201997%20IEEE%20International%20Conference%20on%20Evolutionary%20Computation%20(ICEC%20'97)&rft.au=Lobo,%20F.G.&rft.date=1997&rft.spage=121&rft.epage=125&rft.pages=121-125&rft.isbn=0780339495&rft.isbn_list=9780780339491&rft_id=info:doi/10.1109/ICEC.1997.592281&rft_dat=%3Cieee_6IE%3E592281%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=592281&rfr_iscdi=true |