Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore framework

Abstract : We present a framework to build a multiobjective algorithm from single-objective ones. This framework addresses the p × n-dimensional problem of finding p solutions in an n-dimensional search space, maximizing an indicator by dynamic subspace optimization. Each single-objective algorithm optimizes the indicator function given p − 1 fixed solutions. Crucially, dominated solutions minimize their distance to the empirical Pareto front defined by these p − 1 solutions. We instantiate the framework with CMA-ES as single-objective optimizer. The new algorithm, COMO-CMA-ES, is empirically shown to converge linearly on bi-objective convex-quadratic problems and is compared to MO-CMA-ES, NSGA-II and SMS-EMOA.
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https://hal.inria.fr/hal-02103694
Contributor : Cheikh Touré <>
Submitted on : Thursday, April 18, 2019 - 4:09:03 PM
Last modification on : Sunday, April 21, 2019 - 1:16:56 AM

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Cheikh Touré, Nikolaus Hansen, Anne Auger, Dimo Brockhoff. Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore framework. GECCO '19 - Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. ⟨10.1145/3321707.3321852⟩. ⟨hal-02103694⟩

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