Markov chain models of parallel genetic algorithms
Implementations of parallel genetic algorithms (GA) with multiple populations are common, but they introduce several parameters whose effect on the quality of the search is not well understood. Parameters such as the number of populations, their size, the topology of communications, and the migratio...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on evolutionary computation 2000-09, Vol.4 (3), p.216-226 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 226 |
|---|---|
| container_issue | 3 |
| container_start_page | 216 |
| container_title | IEEE transactions on evolutionary computation |
| container_volume | 4 |
| creator | Cantu-Paz, E. |
| description | Implementations of parallel genetic algorithms (GA) with multiple populations are common, but they introduce several parameters whose effect on the quality of the search is not well understood. Parameters such as the number of populations, their size, the topology of communications, and the migration rate have to be set carefully to reach adequate solutions. This paper presents models that predict the effects of the parallel GA parameters on its search quality. The paper reviews some recent results on the case where each population is connected to all the others and the migration rate is set to the maximum value possible. This bounding case is the simplest to analyze, and it introduces the methodology that is used in the remainder of the paper to analyze parallel GA with arbitrary migration rates and communication topologies. This investigation considers that migration occurs only after each population converges; then, incoming individuals are incorporated into the populations and the algorithm restarts. The models find the probability that each population converges to the correct solution after each restart, and also calculate the long-run chance of success. The accuracy of the models is verified with experiments using one additively decomposable function. |
| doi_str_mv | 10.1109/4235.873233 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_27594497</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>873233</ieee_id><sourcerecordid>914637707</sourcerecordid><originalsourceid>FETCH-LOGICAL-c399t-5115fb7fda412eb41fdee4b57fb639ed44c2919478c6151141c3974981c5336c3</originalsourceid><addsrcrecordid>eNqF0c1LwzAYBvAiCs7pyZunoqAH6cxnkxxl-AUTLwreQpomW2fWzKQT_O9N6VDwoKcE8nsfkjxZdgzBBEIgrgjCdMIZRhjvZCMoCCwAQOVu2gMuCsb46352EOMSAEgoFKMMParw5j9yvVBNm698bVzMvc3XKijnjMvnpjVdo3Pl5j403WIVD7M9q1w0R9t1nL3c3jxP74vZ093D9HpWaCxEV1AIqa2YrRWByFQE2toYUlFmqxILUxOikUhXZFyXMGEC0xwjgkNNMS41HmcXQ-46-PeNiZ1cNVEb51Rr_CZKAUmJGQMsyfM_JeIEISTA_5BRQYjoE09_waXfhDY9V3JOAUGA8IQuB6SDjzEYK9ehWanwKSGQfR-y70MOfSR9to1UUStng2p1E79HePoB1KuTQTXGmJ_DIeILjcWOyw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>885042048</pqid></control><display><type>article</type><title>Markov chain models of parallel genetic algorithms</title><source>IEEE Electronic Library (IEL)</source><creator>Cantu-Paz, E.</creator><creatorcontrib>Cantu-Paz, E.</creatorcontrib><description>Implementations of parallel genetic algorithms (GA) with multiple populations are common, but they introduce several parameters whose effect on the quality of the search is not well understood. Parameters such as the number of populations, their size, the topology of communications, and the migration rate have to be set carefully to reach adequate solutions. This paper presents models that predict the effects of the parallel GA parameters on its search quality. The paper reviews some recent results on the case where each population is connected to all the others and the migration rate is set to the maximum value possible. This bounding case is the simplest to analyze, and it introduces the methodology that is used in the remainder of the paper to analyze parallel GA with arbitrary migration rates and communication topologies. This investigation considers that migration occurs only after each population converges; then, incoming individuals are incorporated into the populations and the algorithm restarts. The models find the probability that each population converges to the correct solution after each restart, and also calculate the long-run chance of success. The accuracy of the models is verified with experiments using one additively decomposable function.</description><identifier>ISSN: 1089-778X</identifier><identifier>EISSN: 1941-0026</identifier><identifier>DOI: 10.1109/4235.873233</identifier><identifier>CODEN: ITEVF5</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Computer science ; Contracts ; Exact sciences and technology ; General topology ; Genetic algorithms ; Guidelines ; Mathematical analysis ; Mathematical methods in physics ; Mathematical models ; Mathematics ; Migration ; Numerical approximation and analysis ; Numerical optimization ; Operational research and scientific management ; Operational research. Management science ; Optimization. Search problems ; Physics ; Populations ; Predictive models ; Probability ; Sciences and techniques of general use ; Scientific computing ; Searching ; Stochastic processes ; Studies ; Time measurement ; Topology ; Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds</subject><ispartof>IEEE transactions on evolutionary computation, 2000-09, Vol.4 (3), p.216-226</ispartof><rights>2001 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2000</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-5115fb7fda412eb41fdee4b57fb639ed44c2919478c6151141c3974981c5336c3</citedby><cites>FETCH-LOGICAL-c399t-5115fb7fda412eb41fdee4b57fb639ed44c2919478c6151141c3974981c5336c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/873233$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/873233$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=811423$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Cantu-Paz, E.</creatorcontrib><title>Markov chain models of parallel genetic algorithms</title><title>IEEE transactions on evolutionary computation</title><addtitle>TEVC</addtitle><description>Implementations of parallel genetic algorithms (GA) with multiple populations are common, but they introduce several parameters whose effect on the quality of the search is not well understood. Parameters such as the number of populations, their size, the topology of communications, and the migration rate have to be set carefully to reach adequate solutions. This paper presents models that predict the effects of the parallel GA parameters on its search quality. The paper reviews some recent results on the case where each population is connected to all the others and the migration rate is set to the maximum value possible. This bounding case is the simplest to analyze, and it introduces the methodology that is used in the remainder of the paper to analyze parallel GA with arbitrary migration rates and communication topologies. This investigation considers that migration occurs only after each population converges; then, incoming individuals are incorporated into the populations and the algorithm restarts. The models find the probability that each population converges to the correct solution after each restart, and also calculate the long-run chance of success. The accuracy of the models is verified with experiments using one additively decomposable function.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Computer science</subject><subject>Contracts</subject><subject>Exact sciences and technology</subject><subject>General topology</subject><subject>Genetic algorithms</subject><subject>Guidelines</subject><subject>Mathematical analysis</subject><subject>Mathematical methods in physics</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Migration</subject><subject>Numerical approximation and analysis</subject><subject>Numerical optimization</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Optimization. Search problems</subject><subject>Physics</subject><subject>Populations</subject><subject>Predictive models</subject><subject>Probability</subject><subject>Sciences and techniques of general use</subject><subject>Scientific computing</subject><subject>Searching</subject><subject>Stochastic processes</subject><subject>Studies</subject><subject>Time measurement</subject><subject>Topology</subject><subject>Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds</subject><issn>1089-778X</issn><issn>1941-0026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0c1LwzAYBvAiCs7pyZunoqAH6cxnkxxl-AUTLwreQpomW2fWzKQT_O9N6VDwoKcE8nsfkjxZdgzBBEIgrgjCdMIZRhjvZCMoCCwAQOVu2gMuCsb46352EOMSAEgoFKMMParw5j9yvVBNm698bVzMvc3XKijnjMvnpjVdo3Pl5j403WIVD7M9q1w0R9t1nL3c3jxP74vZ093D9HpWaCxEV1AIqa2YrRWByFQE2toYUlFmqxILUxOikUhXZFyXMGEC0xwjgkNNMS41HmcXQ-46-PeNiZ1cNVEb51Rr_CZKAUmJGQMsyfM_JeIEISTA_5BRQYjoE09_waXfhDY9V3JOAUGA8IQuB6SDjzEYK9ehWanwKSGQfR-y70MOfSR9to1UUStng2p1E79HePoB1KuTQTXGmJ_DIeILjcWOyw</recordid><startdate>20000901</startdate><enddate>20000901</enddate><creator>Cantu-Paz, E.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20000901</creationdate><title>Markov chain models of parallel genetic algorithms</title><author>Cantu-Paz, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-5115fb7fda412eb41fdee4b57fb639ed44c2919478c6151141c3974981c5336c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Computer science</topic><topic>Contracts</topic><topic>Exact sciences and technology</topic><topic>General topology</topic><topic>Genetic algorithms</topic><topic>Guidelines</topic><topic>Mathematical analysis</topic><topic>Mathematical methods in physics</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Migration</topic><topic>Numerical approximation and analysis</topic><topic>Numerical optimization</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Optimization. Search problems</topic><topic>Physics</topic><topic>Populations</topic><topic>Predictive models</topic><topic>Probability</topic><topic>Sciences and techniques of general use</topic><topic>Scientific computing</topic><topic>Searching</topic><topic>Stochastic processes</topic><topic>Studies</topic><topic>Time measurement</topic><topic>Topology</topic><topic>Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cantu-Paz, E.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on evolutionary computation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Cantu-Paz, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Markov chain models of parallel genetic algorithms</atitle><jtitle>IEEE transactions on evolutionary computation</jtitle><stitle>TEVC</stitle><date>2000-09-01</date><risdate>2000</risdate><volume>4</volume><issue>3</issue><spage>216</spage><epage>226</epage><pages>216-226</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><coden>ITEVF5</coden><abstract>Implementations of parallel genetic algorithms (GA) with multiple populations are common, but they introduce several parameters whose effect on the quality of the search is not well understood. Parameters such as the number of populations, their size, the topology of communications, and the migration rate have to be set carefully to reach adequate solutions. This paper presents models that predict the effects of the parallel GA parameters on its search quality. The paper reviews some recent results on the case where each population is connected to all the others and the migration rate is set to the maximum value possible. This bounding case is the simplest to analyze, and it introduces the methodology that is used in the remainder of the paper to analyze parallel GA with arbitrary migration rates and communication topologies. This investigation considers that migration occurs only after each population converges; then, incoming individuals are incorporated into the populations and the algorithm restarts. The models find the probability that each population converges to the correct solution after each restart, and also calculate the long-run chance of success. The accuracy of the models is verified with experiments using one additively decomposable function.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/4235.873233</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1089-778X |
| ispartof | IEEE transactions on evolutionary computation, 2000-09, Vol.4 (3), p.216-226 |
| issn | 1089-778X 1941-0026 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_27594497 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Applied sciences Computer science Contracts Exact sciences and technology General topology Genetic algorithms Guidelines Mathematical analysis Mathematical methods in physics Mathematical models Mathematics Migration Numerical approximation and analysis Numerical optimization Operational research and scientific management Operational research. Management science Optimization. Search problems Physics Populations Predictive models Probability Sciences and techniques of general use Scientific computing Searching Stochastic processes Studies Time measurement Topology Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds |
| title | Markov chain models of parallel genetic algorithms |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T05%3A57%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Markov%20chain%20models%20of%20parallel%20genetic%20algorithms&rft.jtitle=IEEE%20transactions%20on%20evolutionary%20computation&rft.au=Cantu-Paz,%20E.&rft.date=2000-09-01&rft.volume=4&rft.issue=3&rft.spage=216&rft.epage=226&rft.pages=216-226&rft.issn=1089-778X&rft.eissn=1941-0026&rft.coden=ITEVF5&rft_id=info:doi/10.1109/4235.873233&rft_dat=%3Cproquest_RIE%3E914637707%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=885042048&rft_id=info:pmid/&rft_ieee_id=873233&rfr_iscdi=true |