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 Jg. 206; H. 2; S. 417 - 425
Hauptverfasser:	Fowler, John W., Gel, Esma S., Köksalan, Murat M., Korhonen, Pekka, Marquis, Jon L., Wallenius, Jyrki
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 16.10.2010 Elsevier Elsevier Sequoia S.A
Schriftenreihe:	European Journal of Operational Research
Schlagworte:	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 Multi-objective optimization Interactive optimization Evolutionary optimization Knapsack problem Concave programming Cone Decision making Evolutionary algorithm Multiobjective programming Partial ordering Optimization Concave function Genetic algorithm Preference
ISSN:	0377-2217, 1872-6860
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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. 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.
AbstractList	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. 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. [PUBLICATION ABSTRACT]
Author	Fowler, John W. Köksalan, Murat M. Gel, Esma S. Korhonen, Pekka Marquis, Jon L. Wallenius, Jyrki
Author_xml	– sequence: 1 givenname: John W. surname: Fowler fullname: Fowler, John W. organization: Arizona State University, P.O. Box 875906, Tempe, AZ 85287-5906, USA – sequence: 2 givenname: Esma S. surname: Gel fullname: Gel, Esma S. organization: Arizona State University, P.O. Box 875906, Tempe, AZ 85287-5906, USA – sequence: 3 givenname: Murat M. surname: Köksalan fullname: Köksalan, Murat M. organization: Middle East Technical University, 06531 Ankara, Turkey – sequence: 4 givenname: Pekka surname: Korhonen fullname: Korhonen, Pekka organization: Aalto University School of Economics, P.O. Box 21210, 00076 AALTO, Finland – sequence: 5 givenname: Jon L. surname: Marquis fullname: Marquis, Jon L. organization: Arizona State University, P.O. Box 875906, Tempe, AZ 85287-5906, USA – sequence: 6 givenname: Jyrki surname: Wallenius fullname: Wallenius, Jyrki email: Jyrki.Wallenius@hse.fi organization: Aalto University School of Economics, P.O. Box 21210, 00076 AALTO, Finland
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Keywords	Multi-objective optimization Interactive optimization Evolutionary optimization Knapsack problem Concave programming Cone Decision making Evolutionary algorithm Multiobjective programming Partial ordering Optimization Concave function Genetic algorithm Preference
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Snippet	We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function...
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SubjectTerms	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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