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Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions

Nikolaus Hansen 1, 2 Steffen Finck 3 Raymond Ros 1, 4 Anne Auger 1
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
Abstract : Quantifying and comparing performance of optimization algorithms is one important aspect of research in search and optimization. However, this task turns out to be tedious and difficult to realize even in the single-objective case -- at least if one is willing to accomplish it in a scientifically decent and rigorous way. The BBOB 2009 workshop will furnish most of this tedious task for its participants: (1) choice and implementation of a well-motivated real-parameter benchmark function testbed, (2) design of an experimental set-up, (3) generation of data output for (4) post-processing and presentation of the results in graphs and tables. What remains to be done for the participants is to allocate CPU-time, run their favorite black-box real-parameter optimizer in a few dimensions a few hundreds of times and execute the provided post-processing script afterwards. In this report, the testbed of noise-free functions is defined and motivated.
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Submitted on : Monday, February 18, 2019 - 4:52:04 PM
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  • HAL Id : inria-00362633, version 2



Nikolaus Hansen, Steffen Finck, Raymond Ros, Anne Auger. Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions. [Research Report] RR-6829, INRIA. 2009. ⟨inria-00362633v2⟩



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