Using evolutionary computation to infer the decision maker's preference model in presence of imperfect knowledge: A case study in portfolio optimization.

It is usually very difficult to elicit the parameter values of models representing decision makers' preferences. Consequently, some imprecision, ill-determination and arbitrariness are unavoidable. Moreover, such elicitation cannot be performed by traditional optimization techniques in a reasonable time. Therefore, we present here a novel elicitation method guided by a genetic algorithm whose main contribution is coping with imperfect knowledge. The latter is done by using interval numbers representing all the possible values that the parameters can attain. The assessment of the method showed its high ability to reproduce the decision maker's preferences. Finally, as the method proposed in this paper is the complement of the authors' previous work regarding the optimization of stock portfolios, we provide a case study in such a field. We use differential evolution to obtain the most satisfactory portfolio. The results reported here show that the best portfolio returns are obtained when the elicitation method is exploited, and we conclude that the new overall approach might be an interesting alternative to the already-existing methods. • We present a novel method to infer the parameters of decision makers' systems of preferences using evolutionary algorithms. • The method's main contribution is coping with imperfect knowledge on the decision maker's preferences and criterion scores. • An application in an extensive case study on optimization of stock portfolios is provided. • The results support the convenience of the proposed method in situations of uncertainty over all the benchmarks used. [ABSTRACT FROM AUTHOR]