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Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm

François-Michel De Rainville, Christian Gagné, Olivier Teytaud and Denis Laurendeau

Abstract - Many fields rely on some stochastic sampling of a given complex space. Low-discrepancy sequences are methods aiming at producing samples with better space-filling properties than uniformly distributed random numbers, hence allowing a more efficient sampling of that space. State-of-the-art methods like nearly orthogonal Latin hypercubes and scrambled Halton sequences are configured by permutations of internal parameters, where permutations are commonly done randomly. This paper proposes the use of evolutionary algorithms to evolve these permutations, in order to optimize a discrepancy measure. Results show that an evolutionary method is able to generate low-discrepancy sequences of significantly better space-filling properties compared to sequences configured with purely random permutations.

Bibtex:

@inproceedings{Rainville775,
author    = { François-Michel De Rainville and Christian Gagné and Olivier Teytaud and Denis Laurendeau },
title     = { Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm },
booktitle = { Proc. of Genetic and Evolutionary Computation Conference (GECCO 2009) },
year      = { 2009 },
location  = { Montreal (Quebec), Canada }
}

Dernière modification: 2009/04/15 par cgagne

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