Benchmarking RM-MEDA on the Bi-objective BBOB-2016 Test Suite

Anne Auger 1 Dimo Brockhoff 2 Nikolaus Hansen 1 Dejan Tušar 2 Tea Tušar 2 Tobias Wagner 3
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
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 : In this paper, we benchmark the Regularity Model-Based Multiobjective Estimation of Distribution Algorithm (RM-MEDA) of Zhang et al. on the bi-objective bbob-biobj test suite of the Comparing Continuous Optimizers (COCO) platform. It turns out that, starting from about 200 times dimension many function evaluations, RM-MEDA shows a linear increase in the solved hypervolume-based target values with time until a stagnation of the performance occurs rather quickly on all problems. The final percentage of solved hy-pervolume targets seems to decrease with the problem dimension .
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https://hal.inria.fr/hal-01435449
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Submitted on : Saturday, January 14, 2017 - 12:35:03 AM
Last modification on : Friday, March 22, 2019 - 1:34:47 AM
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Anne Auger, Dimo Brockhoff, Nikolaus Hansen, Dejan Tušar, Tea Tušar, et al.. Benchmarking RM-MEDA on the Bi-objective BBOB-2016 Test Suite. GECCO 2016 - Genetic and Evolutionary Computation Conference, Jul 2016, Denver, CO, United States. pp.1241-1247, ⟨10.1145/2908961.2931707⟩. ⟨hal-01435449⟩

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