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Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm
Abstract : 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.
Cited literature [22 references]
https://hal.inria.fr/inria-00386475
Contributor : Olivier Teytaud <>
Submitted on : Thursday, May 21, 2009 - 10:15:49 PM
Last modification on : Thursday, July 8, 2021 - 3:48:12 AM
Long-term archiving on: : Monday, October 15, 2012 - 10:51:22 AM
Contributor : Olivier Teytaud <>
Submitted on : Thursday, May 21, 2009 - 10:15:49 PM
Last modification on : Thursday, July 8, 2021 - 3:48:12 AM
Long-term archiving on: : Monday, October 15, 2012 - 10:51:22 AM
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