Chaotic GEP algorithm for dynamic multi-objective optimization
Dynamic Multi-objective Optimization (DMO) is a new research topic in the field of evolutionary computation in recent years. As Gene Expression Programming (GEP) has a powerful search capability, a new algorithm for DMO called D-GEP Chaotic NSGA-II is proposed. The algorithm is designed on the class...
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| creator | Weihong Wang Yanye Du Qu Li Zhaolin Fang |
| description | Dynamic Multi-objective Optimization (DMO) is a new research topic in the field of evolutionary computation in recent years. As Gene Expression Programming (GEP) has a powerful search capability, a new algorithm for DMO called D-GEP Chaotic NSGA-II is proposed. The algorithm is designed on the classic multi-objective optimization algorithm NSGA-II to make it suitable for DMO, while using GEP for encoding and chaotic variables for generating initial population. The experiments on test problems of three different types have shown that the algorithm has better performance on convergence, diversity and the breadth of the distribution. |
| doi_str_mv | 10.1109/ICNC.2011.6022293 |
| format | Conference Proceeding |
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The experiments on test problems of three different types have shown that the algorithm has better performance on convergence, diversity and the breadth of the distribution.</description><subject>Algorithm design and analysis</subject><subject>Chaos</subject><subject>Chaotic Optimization</subject><subject>Convergence</subject><subject>Dynamic Multi-objective Optimization (DMO)</subject><subject>Gene expression</subject><subject>Gene Expression Programming (GEP)</subject><subject>Heuristic algorithms</subject><subject>Optimization</subject><subject>Programming</subject><issn>2157-9555</issn><isbn>9781424499502</isbn><isbn>142449950X</isbn><isbn>9781424499526</isbn><isbn>1424499534</isbn><isbn>9781424499533</isbn><isbn>1424499526</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkM1KAzEURiMqWOo8gLiZF5jx3vw2G0FCrYWiLrovyUxiU2aaMhOF-vRa7MbV4ePAtziE3CHUiKAflubV1BQQawmUUs0uSKHVDDnlXGtB5eW_DfSKTCgKVWkhxA0pxnEHAAyVUqAn5NFsbcqxKRfz99J2H2mIeduXIQ1le9zb_tf0n12OVXI73-T45ct0yLGP3zbHtL8l18F2oy_OnJL183xtXqrV22JpnlZV1JArQZkKClvk3nnUMGsZNk4FK0HKwJVrPFeeAXWIXkGQUqBlzAl7Eq1gU3L_dxu995vDEHs7HDfnAOwHj31MBA</recordid><startdate>201107</startdate><enddate>201107</enddate><creator>Weihong Wang</creator><creator>Yanye Du</creator><creator>Qu Li</creator><creator>Zhaolin Fang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201107</creationdate><title>Chaotic GEP algorithm for dynamic multi-objective optimization</title><author>Weihong Wang ; Yanye Du ; Qu Li ; Zhaolin Fang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-5237f71d14ebe1908d31cb7fa6066f47bce47e302b11e70f6651a33b5ace47d53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithm design and analysis</topic><topic>Chaos</topic><topic>Chaotic Optimization</topic><topic>Convergence</topic><topic>Dynamic Multi-objective Optimization (DMO)</topic><topic>Gene expression</topic><topic>Gene Expression Programming (GEP)</topic><topic>Heuristic algorithms</topic><topic>Optimization</topic><topic>Programming</topic><toplevel>online_resources</toplevel><creatorcontrib>Weihong Wang</creatorcontrib><creatorcontrib>Yanye Du</creatorcontrib><creatorcontrib>Qu Li</creatorcontrib><creatorcontrib>Zhaolin Fang</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>Weihong Wang</au><au>Yanye Du</au><au>Qu Li</au><au>Zhaolin Fang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Chaotic GEP algorithm for dynamic multi-objective optimization</atitle><btitle>2011 Seventh International Conference on Natural Computation</btitle><stitle>ICNC</stitle><date>2011-07</date><risdate>2011</risdate><volume>2</volume><spage>1067</spage><epage>1071</epage><pages>1067-1071</pages><issn>2157-9555</issn><isbn>9781424499502</isbn><isbn>142449950X</isbn><eisbn>9781424499526</eisbn><eisbn>1424499534</eisbn><eisbn>9781424499533</eisbn><eisbn>1424499526</eisbn><abstract>Dynamic Multi-objective Optimization (DMO) is a new research topic in the field of evolutionary computation in recent years. As Gene Expression Programming (GEP) has a powerful search capability, a new algorithm for DMO called D-GEP Chaotic NSGA-II is proposed. The algorithm is designed on the classic multi-objective optimization algorithm NSGA-II to make it suitable for DMO, while using GEP for encoding and chaotic variables for generating initial population. The experiments on test problems of three different types have shown that the algorithm has better performance on convergence, diversity and the breadth of the distribution.</abstract><pub>IEEE</pub><doi>10.1109/ICNC.2011.6022293</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2011 Seventh International Conference on Natural Computation, 2011, Vol.2, p.1067-1071 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Chaos Chaotic Optimization Convergence Dynamic Multi-objective Optimization (DMO) Gene expression Gene Expression Programming (GEP) Heuristic algorithms Optimization Programming |
| title | Chaotic GEP algorithm for dynamic multi-objective optimization |
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