Skip to Main content Skip to Navigation
Conference papers

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

Olivier Teytaud 1, 2, * 
* Corresponding author
2 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 : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download
Contributor : Olivier Teytaud Connect in order to contact the contributor
Submitted on : Monday, September 7, 2015 - 11:10:05 AM
Last modification on : Thursday, July 8, 2021 - 3:46:38 AM
Long-term archiving on: : Tuesday, December 8, 2015 - 11:15:43 AM


Files produced by the author(s)


  • HAL Id : hal-01194542, version 1


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



Record views


Files downloads