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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Citation Formats

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.