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Conference Papers Year : 2009

Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm


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.
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inria-00386475 , version 1 (21-05-2009)


  • HAL Id : inria-00386475 , version 1


François-Michel De Rainville, Christian Gagné, Olivier Teytaud, Denis Laurendeau. Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm. Genetic and Evolutionary Computation Conference, 2009, Montréal, Canada. 8 p. ⟨inria-00386475⟩
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