Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite

Oswin Krause 1 Tobias Glasmachers 2 Nikolaus Hansen 3 Christian Igel 1
3 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 : The unbounded population multi-objective covariance matrix adaptation evolution strategy (UP-MO-CMA-ES) aims at maximizing the total hypervolume covered by all evaluated points. It adds all non-dominated solutions found to its population and employs Gaussian mutations with adaptive covariance matrices to also solve ill-conditioned problems. A novel recombination operator adapts the covariance matrices to point along the Pareto front. The UP-MO-CMA-ES is combined with a parallel exploration strategy and empirically evaluated on the bi-objective BBOB-biobj benchmark problems. Results show that the algorithm can reliably solve ill-conditioned problems as well as weakly-structured problems. However, it is less suited for the rugged multi-modal objective functions in the benchmark.
Keywords : CMA-ES MO-CMA-ES
Type de document :
Communication dans un congrès
GECCO'16 - Companion of Proceedings of the 2016 Genetic and Evolutionary Computation Conference, Jul 2016, Denver, United States. pp.1177-1184, 〈http://gecco-2016.sigevo.org/〉. 〈10.1145/2908961.2931699〉
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https://hal.inria.fr/hal-01381653
Contributeur : Nikolaus Hansen <>
Soumis le : vendredi 14 octobre 2016 - 15:03:11
Dernière modification le : jeudi 5 avril 2018 - 12:30:12

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Oswin Krause, Tobias Glasmachers, Nikolaus Hansen, Christian Igel. Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite. GECCO'16 - Companion of Proceedings of the 2016 Genetic and Evolutionary Computation Conference, Jul 2016, Denver, United States. pp.1177-1184, 〈http://gecco-2016.sigevo.org/〉. 〈10.1145/2908961.2931699〉. 〈hal-01381653〉

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