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Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm
François-Michel De Rainville1, Christian Gagné1, Olivier Teytaud2, 3, 4, Denis Laurendeau1

Many elds rely on some stochastic sampling of a given com- plex space. Low-discrepancy sequences are methods aim- ing at producing samples with better space-lling properties than uniformly distributed random numbers, hence allow- ing a more ecient sampling of that space. State-of-the-art methods like nearly orthogonal Latin hypercubes and scram- bled Halton sequences are congured by permutations of in- ternal parameters, where permutations are commonly done randomly. This paper proposes the use of evolutionary al- gorithms to evolve these permutations, in order to optimize a discrepancy measure. Results show that an evolution- ary method is able to generate low-discrepancy sequences of signicantly better space-lling properties compared to sequences congured with purely random permutations.
1:  GEL-GIF - Département de génie électrique et de génie informatique
2:  INRIA Futurs - TAO
3:  LRI - Laboratoire de Recherche en Informatique
4:  INRIA Saclay - Ile de France - TAO