1. Arizona State University, P.O. Box 875906, Tempe, AZ 85287-5906, USA;2. Middle East Technical University, 06531 Ankara, Turkey;3. Aalto University School of Economics, P.O. Box 21210, 00076 AALTO, Finland
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.