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Quasi-random numbers improve the CMA-ES on the BBOB testbed

Olivier Teytaud 1, 2, *
* Corresponding author
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 : Pseudo-random numbers are usually a good enough approximation of random numbers in evolutionary algorithms. But quasi-random numbers follow a different idea, namely they are aimed at being more regularly distributed than random points. It has been pointed out in earlier papers that quasi-random points provide a significant improvement in evolutionary optimization. In this paper, we experiment quasi-random mutations on a well known test case, namely the Coco/Bbob test case. We also include experiments on translated or rescaled versions of BBOB, on which we get similar improvements.
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https://hal.inria.fr/hal-01194542
Contributor : Olivier Teytaud <>
Submitted on : Monday, September 7, 2015 - 11:10:05 AM
Last modification on : Friday, April 30, 2021 - 10:01:09 AM
Long-term archiving on: : Tuesday, December 8, 2015 - 11:15:43 AM

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  • HAL Id : hal-01194542, version 1

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Olivier Teytaud. Quasi-random numbers improve the CMA-ES on the BBOB testbed. Artificial Evolution (EA2015), 2015, Lyon, France. pp.13. ⟨hal-01194542⟩

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