Högskolan i Skövde

In recent years, interactive evolutionary multiobjective optimization methods have been getting more and more attention. In these methods, a decision maker, who is a domain expert, is iteratively involved in the solution process and guides the solution process toward her/his desired region with preference information. However, there have not been many studies regarding the performance evaluation of interactive evolutionary methods. On the other hand, indicators have been developed for a priori methods, where the DM provides preference information before optimization. In the literature, some studies treat interactive evolutionary methods as a series of a priori steps when assessing and comparing them. In such settings, indicators designed for a priori methods can be utilized. In this paper, we propose a novel performance indicator for interactive evolutionary multiobjective optimization methods and show how it can assess the performance of these interactive methods as a whole process and not as a series of separate steps. In addition, we demonstrate the shortcomings of using indicators designed for a priori methods for comparing interactive evolutionary methods. IEEE