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.

通常很难得出代表决策者偏好的模型的参数值。因此，不可避免的是某些不精确，不确定性和任意性。此外，这种启发不能通过传统的优化技术在合理的时间内执行。因此，我们在这里提出一种新的基于遗传算法的启发方法，其主要贡献是应对不完善的知识。后者是通过使用表示参数可以达到的所有可能值的间隔号来完成的。该方法的评估表明，它具有重现决策者偏好的能力。最后，由于本文提出的方法是作者先前有关股票投资组合优化工作的补充，因此我们在此领域提供了一个案例研究。我们使用差分进化来获得最满意的投资组合。此处报告的结果表明，当使用启发方法时，可以获得最佳的投资组合收益，并且我们得出结论，新的总体方法可能是现有方法的一种有趣的替代方法。