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Interactive evolutionary multi-objective optimization for quasi-concave preference functions
Date
2010-10-16
Author
Fowler, John W.
Gel, Esma S.
Köksalan, Mustafa Murat
Korhonen, Pekka
Marquis, Jon L.
Wallenius, Jyrki
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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 its 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. (C) 2010 Elsevier By. All rights reserved.
Subject Keywords
Interactive optimization
,
Multi-objective optimization
,
Evolutionary optimization
,
Knapsack problem
URI
https://hdl.handle.net/11511/57452
Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
DOI
https://doi.org/10.1016/j.ejor.2010.02.027
Collections
Department of Industrial Engineering, Article
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BibTeX
J. W. Fowler, E. S. Gel, M. M. Köksalan, P. Korhonen, J. L. Marquis, and J. Wallenius, “Interactive evolutionary multi-objective optimization for quasi-concave preference functions,”
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
, pp. 417–425, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57452.