Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed

Anne Auger 1 Dimo Brockhoff 2 Nikolaus Hansen 1 Dejan Tušar 2 Tea Tušar 2 Tobias Wagner 3
1 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
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : The Comparing Continuous Optimizers platform COCO has become a standard for benchmarking numerical (single-objective) optimization algorithms effortlessly. In 2016, COCO has been extended towards multi-objective optimization by providing a first bi-objective test suite. To provide a baseline, we benchmark a pure random search on this bi-objective bbob-biobj test suite of the COCO platform. For each combination of function, dimension n, and instance of the test suite, $10^6 · n$ candidate solutions are sampled uniformly within the sampling box $[−5, 5]^n$ .
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Communication dans un congrès
GECCO 2016 - Genetic and Evolutionary Computation Conference, Jul 2016, Denver, CO, United States. ACM, GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp.1217-1223, 〈10.1145/2908961.2931704〉
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Contributeur : Dimo Brockhoff <>
Soumis le : samedi 14 janvier 2017 - 00:48:07
Dernière modification le : mardi 3 juillet 2018 - 11:27:58
Document(s) archivé(s) le : samedi 15 avril 2017 - 12:26:31

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Anne Auger, Dimo Brockhoff, Nikolaus Hansen, Dejan Tušar, Tea Tušar, et al.. Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed. GECCO 2016 - Genetic and Evolutionary Computation Conference, Jul 2016, Denver, CO, United States. ACM, GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp.1217-1223, 〈10.1145/2908961.2931704〉. 〈hal-01435455〉

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