Interactive evolutionary multi-objective optimization for quasi-concave preference functions

John Fowler, Esma Gel, Murat M. Köksalan, Pekka Korhonen, Jon L. Marquis, Jyrki Wallenius

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)417-425
Number of pages9
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - Oct 16 2010


  • Evolutionary optimization
  • Interactive optimization
  • Knapsack problem
  • Multi-objective optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


Dive into the research topics of 'Interactive evolutionary multi-objective optimization for quasi-concave preference functions'. Together they form a unique fingerprint.

Cite this