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Multi-objective optimization problems are usually solved with evolutionary algorithms when the objective functions are cheap to compute, or with surrogate-based optimizers otherwise. In the latter case, the objective functions are modeled with powerful non-linear model learners such as Gaussian Processes or Support Vector Machines, for which the training time can be prohibitively large when dealing with optimization problems with moderately expensive objective functions. In this paper, wedoi:10.1145/2463372.2463455 dblp:conf/gecco/VerbeeckMGB13 fatcat:nu2asa32efgj7favo72zw3xoba