HypE: an algorithm for fast hypervolume-based many-objective optimization

Evolutionary Computation
Johannes Bader, Eckart Zitzler

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 the indicator value of the dominant set will also 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 the full exploitation of this indicator's potential; current hypervolume-based search algorithms are limited to problems with only a few objectives. This paper addresses this issue and proposes a fast search algorithm that uses Monte Carlo simulation to approximate the exact hypervolume values. The main idea is not that the actual indicator values are important, but rather that the rankings of solutions induced by the hypervolume indicator. In detail, we present HypE, a hypervolume estimation algorithm for multi-objective optimization, by which the accuracy of the estimates and the available computing resources can be traded off; thereby, not only do many-objective problems become feasible with hyperv...Continue Reading

References

Mar 29, 2007·Evolutionary Computation·Christian IgelStefan Roth

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Citations

Oct 25, 2014·IEEE Transactions on Cybernetics·Ni ChenYu-Song Tan
Jul 12, 2014·IEEE Transactions on Cybernetics·Vui Ann ShimHuajin Tang
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