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A Pareto-Compliant Surrogate Approach for Multiobjective Optimization

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 2, 1
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : This paper discusses the idea of using a single Pareto-compliant surrogate model for multiobjective optimization. While most surrogate approaches to multi-objective optimization build a surrogate model for each objective, the recently proposed mono surrogate 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 aims of this paper are to identify issues of the proposed approach demanding further study and to raise the question of how to efficiently incorporate quality indicators, such as the hypervolume into the surrogate model.
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Submitted on : Monday, May 17, 2010 - 4:06:27 PM
Last modification on : Thursday, June 17, 2021 - 3:47:51 AM
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  • HAL Id : hal-00483996, version 1



Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. A Pareto-Compliant Surrogate Approach for Multiobjective Optimization. Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Jul 2010, Portland, OR, United States. ⟨hal-00483996⟩



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