Incorporation of imprecise goal vectors into evolutionary multi-objective optimization

Abstract

Preference-based techniques in multi-objective evolutionary algorithms (MOEA) are gaining importance. This paper presents a method of representing, eliciting and integrating decision making preference expressed as a set of imprecise goal vectors into a MOEA with steady-state replacement. The specification of a precise goal vector without extensive knowledge of problem behavior often leads to undesirable results. The approach proposed in this paper facilitates the linguistic specification of goal vectors relative to extreme, non-dominated solutions (i.e. the goal is specified as ”Very Small”, ”Small”, ”Medium”, ”Large”, and ”Very Large”) with three degrees of imprecision as desired by the decision maker. The degree of imprecision corresponds to the density of solutions desired within the target subset. Empirical investigations of the proposed method yield promising results.