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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Bibliographic Details
Published in:European journal of operational research Vol. 206; no. 2; pp. 417 - 425
Main Authors: Fowler, John W., Gel, Esma S., Köksalan, Murat M., Korhonen, Pekka, Marquis, Jon L., Wallenius, Jyrki
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 16.10.2010
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
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
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
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2010.02.027