Interactive evolutionary multi-objective optimization for quasi-concave preference functions
We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm. We periodically send solutions to the decision maker (DM) for her evaluation and use the resulting preferen...
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| Veröffentlicht in: | European journal of operational research 2010-10, Vol.206 (2), p.417-425 |
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| container_end_page | 425 |
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| container_issue | 2 |
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| container_title | European journal of operational research |
| container_volume | 206 |
| creator | Fowler, John W. Gel, Esma S. Köksalan, Murat M. Korhonen, Pekka Marquis, Jon L. Wallenius, Jyrki |
| description | We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm. We periodically send solutions to the decision maker (DM) for her evaluation and use the resulting preference information to form preference cones consisting of inferior solutions. The cones allow us to implicitly rank solutions that the DM has not considered. This technique avoids assuming an exact form for the preference function, but does assume that the preference function is quasi-concave. This paper describes the genetic algorithm and demonstrates its performance on the multi-objective knapsack problem. |
| doi_str_mv | 10.1016/j.ejor.2010.02.027 |
| format | Article |
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We periodically send solutions to the decision maker (DM) for her evaluation and use the resulting preference information to form preference cones consisting of inferior solutions. The cones allow us to implicitly rank solutions that the DM has not considered. This technique avoids assuming an exact form for the preference function, but does assume that the preference function is quasi-concave. This paper describes the genetic algorithm and demonstrates its performance on the multi-objective knapsack problem.</description><subject>Applied sciences</subject><subject>Decision making models</subject><subject>Decision theory. Utility theory</subject><subject>Evolutionary optimization</subject><subject>Exact sciences and technology</subject><subject>Flows in networks. Combinatorial problems</subject><subject>Genetic algorithms</subject><subject>Interactive optimization</subject><subject>Interactive optimization Multi-objective optimization Evolutionary optimization Knapsack problem</subject><subject>Knapsack problem</subject><subject>Multi-objective optimization</subject><subject>Operational research and scientific management</subject><subject>Operational research. 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Utility theory</topic><topic>Evolutionary optimization</topic><topic>Exact sciences and technology</topic><topic>Flows in networks. Combinatorial problems</topic><topic>Genetic algorithms</topic><topic>Interactive optimization</topic><topic>Interactive optimization Multi-objective optimization Evolutionary optimization Knapsack problem</topic><topic>Knapsack problem</topic><topic>Multi-objective optimization</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Optimization algorithms</topic><topic>Preferences</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fowler, John W.</creatorcontrib><creatorcontrib>Gel, Esma S.</creatorcontrib><creatorcontrib>Köksalan, Murat M.</creatorcontrib><creatorcontrib>Korhonen, Pekka</creatorcontrib><creatorcontrib>Marquis, Jon L.</creatorcontrib><creatorcontrib>Wallenius, Jyrki</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering 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><jtitle>European journal of operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fowler, John W.</au><au>Gel, Esma S.</au><au>Köksalan, Murat M.</au><au>Korhonen, Pekka</au><au>Marquis, Jon L.</au><au>Wallenius, Jyrki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Interactive evolutionary multi-objective optimization for quasi-concave preference functions</atitle><jtitle>European journal of operational research</jtitle><date>2010-10-16</date><risdate>2010</risdate><volume>206</volume><issue>2</issue><spage>417</spage><epage>425</epage><pages>417-425</pages><issn>0377-2217</issn><eissn>1872-6860</eissn><coden>EJORDT</coden><abstract>We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm. 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| identifier | ISSN: 0377-2217 |
| ispartof | European journal of operational research, 2010-10, Vol.206 (2), p.417-425 |
| issn | 0377-2217 1872-6860 |
| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals |
| subjects | Applied sciences Decision making models Decision theory. Utility theory Evolutionary optimization Exact sciences and technology Flows in networks. Combinatorial problems Genetic algorithms Interactive optimization Interactive optimization Multi-objective optimization Evolutionary optimization Knapsack problem Knapsack problem Multi-objective optimization Operational research and scientific management Operational research. Management science Optimization algorithms Preferences Studies |
| title | Interactive evolutionary multi-objective optimization for quasi-concave preference functions |
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