Evolving MNK-landscapes with structural constraints

In this paper we propose a method for the generation of instances of the MNK-landscapes that maximize different measures used to characterize multi-objective problems. In contrast to previous approaches, the introduced algorithm works by modifying the neighborhood structure of the variables of the M...

Full description

Saved in:
Bibliographic Details
Main Authors: Santana, Roberto, Mendiburu, Alexander, Lozano, Jose A.
Format: Conference Proceeding
Language:English
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1371
container_issue
container_start_page 1364
container_title
container_volume
creator Santana, Roberto
Mendiburu, Alexander
Lozano, Jose A.
description In this paper we propose a method for the generation of instances of the MNK-landscapes that maximize different measures used to characterize multi-objective problems. In contrast to previous approaches, the introduced algorithm works by modifying the neighborhood structure of the variables of the MNK-landscape while keeping fixed the local parameters of its functions. A variant of the algorithm is presented to deal with situations in which the exhaustive enumeration of search space is unfeasible. We show how the introduced method can be used to generate instances with an increased number of solutions in the Pareto front. Furthermore, we investigate whether direct optimization of the correlation between objectives can be used as an indirect method to increase the size of the Pareto fronts of the generated instances.
doi_str_mv 10.1109/CEC.2015.7257047
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_7257047</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7257047</ieee_id><sourcerecordid>7257047</sourcerecordid><originalsourceid>FETCH-LOGICAL-i123t-38ada2a22969d3c25edcbc5aa9c90758053635f84a6a82921eb897af3bfde6ef3</originalsourceid><addsrcrecordid>eNotj8tOwzAQRQ0CibawR2KTH3Dw-BF7ligKD1FgAxK7auI4YBTSKnaL-Hsq0dU9Z3Oky9gliBJA4HXd1KUUYEorjRXaHrE5aItoNUp7zGaAGrgQsjrZs3DIrXXvZ2ye0pcQoA3gjKlmtx52cfwonp4f-UBjlzxtQip-Yv4sUp62Pm8nGgq_HvdGcczpnJ32NKRwcdgFe7ttXut7vny5e6hvljyCVJkrRx1JkhIr7JSXJnS-9YYIPQprnDCqUqZ3mipyEiWE1qGlXrV9F6rQqwW7-u_GEMJqM8Vvmn5Xh7PqDwwkRtI</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Evolving MNK-landscapes with structural constraints</title><source>IEEE Xplore All Conference Series</source><creator>Santana, Roberto ; Mendiburu, Alexander ; Lozano, Jose A.</creator><creatorcontrib>Santana, Roberto ; Mendiburu, Alexander ; Lozano, Jose A.</creatorcontrib><description>In this paper we propose a method for the generation of instances of the MNK-landscapes that maximize different measures used to characterize multi-objective problems. In contrast to previous approaches, the introduced algorithm works by modifying the neighborhood structure of the variables of the MNK-landscape while keeping fixed the local parameters of its functions. A variant of the algorithm is presented to deal with situations in which the exhaustive enumeration of search space is unfeasible. We show how the introduced method can be used to generate instances with an increased number of solutions in the Pareto front. Furthermore, we investigate whether direct optimization of the correlation between objectives can be used as an indirect method to increase the size of the Pareto fronts of the generated instances.</description><identifier>ISSN: 1089-778X</identifier><identifier>EISSN: 1941-0026</identifier><identifier>EISBN: 1479974927</identifier><identifier>EISBN: 9781479974924</identifier><identifier>DOI: 10.1109/CEC.2015.7257047</identifier><language>eng</language><publisher>IEEE</publisher><ispartof>2015 IEEE Congress on Evolutionary Computation (CEC), 2015, p.1364-1371</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7257047$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54796,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7257047$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Santana, Roberto</creatorcontrib><creatorcontrib>Mendiburu, Alexander</creatorcontrib><creatorcontrib>Lozano, Jose A.</creatorcontrib><title>Evolving MNK-landscapes with structural constraints</title><title>2015 IEEE Congress on Evolutionary Computation (CEC)</title><addtitle>CEC</addtitle><description>In this paper we propose a method for the generation of instances of the MNK-landscapes that maximize different measures used to characterize multi-objective problems. In contrast to previous approaches, the introduced algorithm works by modifying the neighborhood structure of the variables of the MNK-landscape while keeping fixed the local parameters of its functions. A variant of the algorithm is presented to deal with situations in which the exhaustive enumeration of search space is unfeasible. We show how the introduced method can be used to generate instances with an increased number of solutions in the Pareto front. Furthermore, we investigate whether direct optimization of the correlation between objectives can be used as an indirect method to increase the size of the Pareto fronts of the generated instances.</description><issn>1089-778X</issn><issn>1941-0026</issn><isbn>1479974927</isbn><isbn>9781479974924</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2015</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj8tOwzAQRQ0CibawR2KTH3Dw-BF7ligKD1FgAxK7auI4YBTSKnaL-Hsq0dU9Z3Oky9gliBJA4HXd1KUUYEorjRXaHrE5aItoNUp7zGaAGrgQsjrZs3DIrXXvZ2ye0pcQoA3gjKlmtx52cfwonp4f-UBjlzxtQip-Yv4sUp62Pm8nGgq_HvdGcczpnJ32NKRwcdgFe7ttXut7vny5e6hvljyCVJkrRx1JkhIr7JSXJnS-9YYIPQprnDCqUqZ3mipyEiWE1qGlXrV9F6rQqwW7-u_GEMJqM8Vvmn5Xh7PqDwwkRtI</recordid><startdate>20150501</startdate><enddate>20150501</enddate><creator>Santana, Roberto</creator><creator>Mendiburu, Alexander</creator><creator>Lozano, Jose A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20150501</creationdate><title>Evolving MNK-landscapes with structural constraints</title><author>Santana, Roberto ; Mendiburu, Alexander ; Lozano, Jose A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i123t-38ada2a22969d3c25edcbc5aa9c90758053635f84a6a82921eb897af3bfde6ef3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2015</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Santana, Roberto</creatorcontrib><creatorcontrib>Mendiburu, Alexander</creatorcontrib><creatorcontrib>Lozano, Jose A.</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 Xplore</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>Santana, Roberto</au><au>Mendiburu, Alexander</au><au>Lozano, Jose A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Evolving MNK-landscapes with structural constraints</atitle><btitle>2015 IEEE Congress on Evolutionary Computation (CEC)</btitle><stitle>CEC</stitle><date>2015-05-01</date><risdate>2015</risdate><spage>1364</spage><epage>1371</epage><pages>1364-1371</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><eisbn>1479974927</eisbn><eisbn>9781479974924</eisbn><abstract>In this paper we propose a method for the generation of instances of the MNK-landscapes that maximize different measures used to characterize multi-objective problems. In contrast to previous approaches, the introduced algorithm works by modifying the neighborhood structure of the variables of the MNK-landscape while keeping fixed the local parameters of its functions. A variant of the algorithm is presented to deal with situations in which the exhaustive enumeration of search space is unfeasible. We show how the introduced method can be used to generate instances with an increased number of solutions in the Pareto front. Furthermore, we investigate whether direct optimization of the correlation between objectives can be used as an indirect method to increase the size of the Pareto fronts of the generated instances.</abstract><pub>IEEE</pub><doi>10.1109/CEC.2015.7257047</doi><tpages>8</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1089-778X
ispartof 2015 IEEE Congress on Evolutionary Computation (CEC), 2015, p.1364-1371
issn 1089-778X
1941-0026
language eng
recordid cdi_ieee_primary_7257047
source IEEE Xplore All Conference Series
title Evolving MNK-landscapes with structural constraints
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T07%3A03%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Evolving%20MNK-landscapes%20with%20structural%20constraints&rft.btitle=2015%20IEEE%20Congress%20on%20Evolutionary%20Computation%20(CEC)&rft.au=Santana,%20Roberto&rft.date=2015-05-01&rft.spage=1364&rft.epage=1371&rft.pages=1364-1371&rft.issn=1089-778X&rft.eissn=1941-0026&rft_id=info:doi/10.1109/CEC.2015.7257047&rft.eisbn=1479974927&rft.eisbn_list=9781479974924&rft_dat=%3Cieee_CHZPO%3E7257047%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i123t-38ada2a22969d3c25edcbc5aa9c90758053635f84a6a82921eb897af3bfde6ef3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=7257047&rfr_iscdi=true