Name
Abstract | ||
|---|---|---|
It was recently proven that sets of points maximizing the hypervolume indicator do not give a good multiplicative approximation of the Pareto front. We introduce a new "logarithmic hypervolume indicator" and prove that it achieves a close-to-optimal multiplicative approximation ratio. This is experimentally verified on several benchmark functions by comparing the approximation quality of the multi-objective covariance matrix evolution strategy (MO-CMA-ES) with the classic hypervolume indicator and the MO-CMA-ES with the logarithmic hypervolume indicator. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1967654.1967662 | Foundations of Genetic Algorithms |
Keywords | Field | DocType |
pareto front,good multiplicative approximation,close-to-optimal multiplicative approximation ratio,hypervolume indicator,multi-objective covariance matrix evolution,performance measures,benchmark function,theory,approximation quality,multiobjective optimization,selection,classic hypervolume indicator,logarithmic hypervolume indicator,covariance matrix,evolution strategy | Mathematical optimization,Multiplicative function,Multi-objective optimization,Evolution strategy,Artificial intelligence,Logarithm,Covariance matrix,Machine learning,Mathematics | Conference |
Citations | PageRank | References |
8 | 0.44 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Friedrich | 1 | 457 | 23.56 |
| Karl Bringmann | 2 | 427 | 30.13 |
| Thomas Voß | 3 | 8 | 0.44 |
| Christian Igel | 4 | 1841 | 123.54 |