Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup

Nikolaus Hansen 1, 2 Anne Auger 1 Steffen Finck 3 Raymond Ros 1, 4
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
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 single-objective 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. Here, the experimental procedure and data formats are thoroughly defined and motivated and the data presentation is touched on.
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[Research Report] RR-6828, INRIA. 2009
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Nikolaus Hansen, Anne Auger, Steffen Finck, Raymond Ros. Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup. [Research Report] RR-6828, INRIA. 2009. 〈inria-00362649v3〉

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