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GECCO
2010
Springer

A mono surrogate for multiobjective optimization

8 years 10 months ago
A mono surrogate for multiobjective optimization
Most surrogate approaches to multi-objective optimization build a surrogate model for each objective. These surrogates can be used inside a classical Evolutionary Multiobjective Optimization Algorithm (EMOA) in lieu of the actual objectives, without modifying the underlying EMOA; or to filter out points that the models predict to be uninteresting. In contrast, the proposed approach aims at building a global surrogate model defined on the decision space and tightly characterizing the current Pareto set and the dominated region, in order to speed up the evolution progress toward the true Pareto set. This surrogate model is specified by combining a One-class Support Vector Machine (SVMs) to characterize the dominated points, and a Regression SVM to clamp the Pareto front on a single value. The resulting surrogate model is then used within state-of-the-art EMOAs to pre-screen the individuals generated by application of standard variation operators. Empirical validation on classical MOO be...
Ilya Loshchilov, Marc Schoenauer, Michèle S
Added 02 Sep 2010
Updated 02 Sep 2010
Type Conference
Year 2010
Where GECCO
Authors Ilya Loshchilov, Marc Schoenauer, Michèle Sebag
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