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The Impact of Sample Volume in Random Search on the bbob Test Suite

Dimo Brockhoff 1, 2, 3 Nikolaus Hansen 1, 2, 3
1 RANDOPT - Randomized Optimisation
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France
Abstract : Uniform Random Search is considered the simplest of all randomized search strategies and thus a natural baseline in benchmarking. Yet, in continuous domain it has its search domain width as a parameter that potentially has a strong effect on its performance. In this paper, we investigate this effect on the well-known 24 functions from the bbob test suite by varying the sample domain of the algorithm ([−α, α]^n for α ∈ {0.5, 1, 2, 3, 4, 5, 6, 10, 20} and n the search space dimension). Though the optima of the bbob testbed are randomly chosen in [−4, 4]^n (with the exception of the linear function f5), the best strategy depends on the search space dimension and the chosen budget. Small budgets and larger dimensions favor smaller domain widths.
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Submitted on : Tuesday, July 2, 2019 - 5:07:27 PM
Last modification on : Monday, March 29, 2021 - 2:47:34 PM

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Dimo Brockhoff, Nikolaus Hansen. The Impact of Sample Volume in Random Search on the bbob Test Suite. GECCO 2019 - The Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. ⟨10.1145/3319619.3326894⟩. ⟨hal-02171213⟩

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