Dominance-Based Pareto-Surrogate for Multi-Objective Optimization

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 1, 2
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Mainstream surrogate approaches for multi-objective problems build one approximation for each objective. Mono-surrogate approaches instead aim at characterizing the Pareto front with a single model. Such an approach has been recently introduced using a mixture of regression Support Vector Machine (SVM) to clamp the current Pareto front to a single value, and one-class SVM to ensure that all dominated points will be mapped on one side of this value. A new mono-surrogate EMO approach is introduced here, relaxing the previous approach and modelling Pareto dominance within the rank-SVM framework. The resulting surrogate model is then used as a filter for offspring generation in standard Evolutionary Multi-Objective Algorithms, and is comparatively validated on a set of benchmark problems.
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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Dominance-Based Pareto-Surrogate for Multi-Objective Optimization. Simulated Evolution And Learning (SEAL-2010), Dec 2010, Kanpur, India. ⟨inria-00522653⟩

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