Loshchilov, IlyaSchoenauer, MarcSebag, Michele2013-04-182013-04-182013-04-18201010.1007/978-3-642-17298-4_24https://infoscience.epfl.ch/handle/20.500.14299/91557Mainstream 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.Multiobjective OptimizationSurrogate ModelsSupport Vector MachineNSGA-IISMS-EMOAMO-CMA-EStext::conference output::conference proceedings::conference paper