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Application of a simple binary genetic algorithm to a noiseless testbed benchmark

Miguel Nicolau 1, 2
2 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 : One of the earliest evolutionary computation algorithms, the genetic algorithm, is applied to the noise-free BBOB 2009 testbed. It is adapted to the continuous domain by increasing the number of bits encoding each variable, until a desired resolution is possible to achieve. Good results and scaling are obtained for separable functions, but poor performance is achieved on the other functions, particularly ill-conditioned functions. Overall running times remain fast throughout.
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Contributor : Miguel Nicolau <>
Submitted on : Monday, April 20, 2009 - 6:53:51 PM
Last modification on : Wednesday, September 16, 2020 - 5:06:41 PM
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  • HAL Id : inria-00377093, version 1



Miguel Nicolau. Application of a simple binary genetic algorithm to a noiseless testbed benchmark. Genetic and Evolutionary Computation Conference (GECCO), Jul 2009, Montreal, Canada. ⟨inria-00377093⟩



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