In the context of Multiple Criteria Decision Aiding, decision makers often face problems with multiple conflicting criteria that justify the use of preference models to help advancing towards a decision. In order to determine the parameters of these preference models, preference elicitation makes use of preference learning algorithms, usually taking as input holistic judgments, i.e., overall preferences on some of the alternatives, expressed by the decision maker. Tools to achieve this goal in the context of a ranking model based on multiple reference profiles are usually based on mixed-integer linear programming, Boolean satisfiability formulation or metaheuristics. However, they are usually unable to handle realistic problems involving many criteria and a large amount of input information. We propose here an evolutionary metaheuristic in order to address this issue. Extensive experiments illustrate its ability to handle problem instances that previous proposals cannot.

在多标准决策辅助的背景下，决策者经常面临多个相互冲突的标准的问题，这些标准证明使用偏好模型来帮助推进决策是合理的。为了确定这些偏好模型的参数，偏好引发利用偏好学习算法，通常将整体判断作为输入，即决策者表达的对某些替代方案的总体偏好。在基于多个参考配置文件的排名模型的背景下实现此目标的工具通常基于混合整数线性规划、布尔可满足性公式或元启发法。然而，它们通常无法处理涉及许多标准和大量输入信息的现实问题。我们在这里提出一种进化元启发法来解决这个问题。大量的实验证明了它能够处理以前的提案无法处理的问题实例。