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The Impact of Variation Operators on the Performance of SMS-EMOA 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
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 - UMR 9189
Abstract : The S-metric-Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) is one of the best-known indicator-based multi-objective optimization algorithms. It employs the S-metric or hypervolume indicator in its (steady-state) selection by deleting in each iteration the solution that has the smallest contribution to the hypervolume indicator. In the SMS-EMOA, the conceptual idea is this hypervolume-based selection. Hence the algorithm can, for example, be combined with several variation operators. Here, we benchmark two versions of SMS-EMOA which employ differential evolution (DE) and simulated binary crossover (SBX) with polynomial mutation (PM) respectively on the newly introduced bi-objective bbob-biobj test suite of the Comparing Continuous Optimizers (COCO) platform. The results un-surprisingly reveal that the choice of the variation operator is crucial for performance with a clear advantage of the DE variant on almost all functions.
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Anne Auger, Dimo Brockhoff, Nikolaus Hansen, Dejan Tušar, Tea Tušar, et al.. The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite. GECCO 2016 - Genetic and Evolutionary Computation Conference, Jul 2016, Denver, CO, United States. pp.1225 - 1232, ⟨10.1145/2908961.2931705⟩. ⟨hal-01435456⟩

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