Multiobjective optimization with a quadratic surrogate-assisted CMA-ES
| Autor: | Gharafi, Mohamed, Hansen, Nikolaus, Brockhoff, Dimo, Le Riche, Rodolphe |
|---|---|
| Přispěvatelé: | Randomized Optimisation (RANDOPT ), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE), Centre National de la Recherche Scientifique (CNRS), GECCO, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Université Clermont Auvergne [2017-2020] (UCA [2017-2020]) |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
multiobjective optimization CMA-ES evolution strategies surrogateassisted optimization quadratic metamodel
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] evolution strategies multiobjective optimization surrogateassisted optimization CMA-ES quadratic metamodel [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] |
| Zdroj: | GECCO 2023-Genetic and Evolutionary Computation Conference GECCO 2023-Genetic and Evolutionary Computation Conference, GECCO, Jul 2023, Lisbon, Portugal. ⟨10.1145/3583131.3590492⟩ Genetic and Evolutionary Computation Conference (GECCO ’23) Genetic and Evolutionary Computation Conference (GECCO ’23), GECCO, Jul 2023, Lisbon, Portugal. ⟨10.1145/3583131.3590492⟩ |
| DOI: | 10.1145/3583131.3590492⟩ |
| Popis: | International audience; We present a surrogate-assisted multiobjective optimization algorithm. The aggregation of the objectives relies on the Uncrowded Hypervolume Improvement (UHVI) which is partly replaced by a linear-quadratic surrogate that is integrated into the CMA-ES algorithm. Surrogating the UHVI poses two challenges. First, the UHVI is a dynamic function, changing with the empirical Pareto set. Second, it is a composite function, defined differently for dominated and nondominated points. The presented algorithm is thought to be used with expensive functions of moderate dimension (up to about 50) with a quadratic surrogate which is updated based on its ranking ability. We report numerical experiments which include tests on the COCO benchmark. The algorithm shows in particular linear convergence on the double sphere function with a convergence rate that is 6-20 times faster than without surrogate assistance. |
| Databáze: | OpenAIRE |
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