Indicator-Based Selection in Multiobjective Search
This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. T...
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| creator | Zitzler, Eckart Künzli, Simon |
| description | This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. It is shown on several continuous and discrete benchmark problems that IBEA can substantially improve on the results generated by two popular algorithms, namely NSGA-II and SPEA2, with respect to different performance measures. |
| doi_str_mv | 10.1007/978-3-540-30217-9_84 |
| format | Conference Proceeding |
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The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. 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Computer arithmetics</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Decision Vector</subject><subject>Exact sciences and technology</subject><subject>Knapsack Problem</subject><subject>Learning and adaptive systems</subject><subject>Multiobjective Evolutionary Algorithm</subject><subject>Optimization Goal</subject><subject>Population Member</subject><subject>Theoretical computing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540230922</isbn><isbn>3540230920</isbn><isbn>3540302174</isbn><isbn>9783540302179</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotUMtOAzEMDC-JUvoHHHrhGEjihCRHqHhUKuIAnCNv4oUty261KUj8PVmKL7ZmxtZ4GDuT4kIKYS-9dRy40YKDUNJyH5zeYydQkD9A77OJvJKSA2h_wGZFP3IKhFfqkE1GFfdWwzGb5bwWpcqWN2bC1LJLTcRtP_AbzJTmz9RS3DZ9N2-6-eNXW8ZqPSLfVDgc4vspO6qxzTT771P2enf7snjgq6f75eJ6xSNos-VKFxu19wltqp1LyqAGk5CiQyOVA20TVc7Z5Ai1ByArQcmKqEZZRYIpO9_d3WCO2NYDdrHJYTM0nzj8hPKwtdLIolM7XS5U90ZDqPr-IwcpwpheKDYChJJH-AsrjOnBL0FHXMY</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Zitzler, Eckart</creator><creator>Künzli, Simon</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2004</creationdate><title>Indicator-Based Selection in Multiobjective Search</title><author>Zitzler, Eckart ; Künzli, Simon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c345t-24978f99da7df88d25a435daec8a5128347deb887d8ea4933e71321beefa1bce3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Decision Vector</topic><topic>Exact sciences and technology</topic><topic>Knapsack Problem</topic><topic>Learning and adaptive systems</topic><topic>Multiobjective Evolutionary Algorithm</topic><topic>Optimization Goal</topic><topic>Population Member</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zitzler, Eckart</creatorcontrib><creatorcontrib>Künzli, Simon</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zitzler, Eckart</au><au>Künzli, Simon</au><au>Smith, Jim</au><au>Lozano, José A.</au><au>Burke, Edmund K.</au><au>Schwefel, Hans-Paul</au><au>Rowe, Jonathan E.</au><au>Yao, Xin</au><au>Merelo-Guervós, Juan Julián</au><au>Bullinaria, John A.</au><au>Tiňo, Peter</au><au>Kabán, Ata</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Indicator-Based Selection in Multiobjective Search</atitle><btitle>Lecture notes in computer science</btitle><date>2004</date><risdate>2004</risdate><spage>832</spage><epage>842</epage><pages>832-842</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540230922</isbn><isbn>3540230920</isbn><eisbn>3540302174</eisbn><eisbn>9783540302179</eisbn><abstract>This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. It is shown on several continuous and discrete benchmark problems that IBEA can substantially improve on the results generated by two popular algorithms, namely NSGA-II and SPEA2, with respect to different performance measures.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/978-3-540-30217-9_84</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2004, p.832-842 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16177151 |
| source | Springer Books |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Computer science control theory systems Decision Vector Exact sciences and technology Knapsack Problem Learning and adaptive systems Multiobjective Evolutionary Algorithm Optimization Goal Population Member Theoretical computing |
| title | Indicator-Based Selection in Multiobjective Search |
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