Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences
Anne Auger, Johannes Bader, Dimo Brockhoff, Eckart Zitzler · 2009
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?