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| Authors: | Dimo Brockhoff, Johannes Bader, Eckart Zitzler |
| Group: | Computer Engineering |
| Type: | Talk |
| Title: | Fast Hypervolume-Based Multiobjective Search Using Monte Carlo Sampling |
| Year: | 2008 |
| Month: | September |
| Pub-Key: | broc2008a |
| Keywords: | EMO |
| Abstract: | In the field of evolutionary multi-criterion optimization, the hypervolume indicator is the only single set quality measure that is known to be strictly monotonic with regard to Pareto dominance: whenever a Pareto set approximation entirely dominates another one, then also the indicator value of the former will be better. This property is of high interest and relevance for problems involving a large number of objective functions. However, the high computational effort required for hypervolume calculation has so far prevented to fully exploit the potential of this indicator; current hypervolume-based search algorithms are limited to problems with only a few objectives. This talk presents a fast search algorithm that uses Monte Carlo sampling to approximate the exact hypervolume values. Experimental results will be provided indicating that this method is highly effective for many-objective problems in comparison to existing multiobjective evolutionary algorithms. |
| Remarks: | Corresponding paper by Johannes Bader and Eckart Zitzler |
| Resources: | [BibTeX] |
