Conditional Preference Networks in Multiobjective Evolutionary Algorithms

Conditional Preference Networks (CP-nets) have emerged as a prominent and intuitive model for representing condi-tional preferences within the AI community.However, the literature lacks a concrete investigation on their applica-bility within Multiobjective Evolutionary Algorithms (MOEAs).Many optimization problems that are tackled by MOEA have a room for conditional preferences on the decision space and thus reasoning about CP-nets in MOEA is of great importance.In this work, we motivate the use of CP-nets within the MOEA framework and provide a sim-ple yet powerful approach that can be augmented during the search to return solutions that are not only optimized for the objectives but also preferred by the decision maker's CPnet.Furthermore, we consider the case where the decision maker has no clear idea about her preferences at the beginning of the search but willing to engage during the search to answer simple comparison questions.Our experimental results show that it is possible to return solutions that are both optimal and preferred even when the search starts with empty preference information.The obtained solution space achieved at least 23% improvement in the average penalty score.