Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences

Optimizing the hypervolume indicator within evolutionary multiobjective optimizers has become popular in the last years. Recently, the indicator has been generalized to the weighted case to incorporate various user preferences into hypervolume-based search algorithms. There are two main open questions in this context: (i) how does the specified weight influence the distribution of a fixed number of points that maximize the weighted hypervolume indicator? (ii) how can the user articulate her preferences easily without specifying a certain weight distribution function? In this paper, we tackle both questions. First, we theoretically investigate optimal distributions of μ points that maximize the weighted hypervolume indicator. Second, based on the obtained theoretical results, we propose a new approach to articulate user preferences within biobjective hypervolumebased optimization in terms of specifying a desired density of points on a predefined (imaginary) Pareto front. Within th...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Anne Auger, Johannes Bader, Dimo Brockhoff, Eckart Zitzler
• Keywords: GECCO 2009, Hypervolume Indicator, Optimization, User Preferences, Weighted Hypervolume Indicator