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 presentation 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.

The CVSL seminars are held on Fridays at 11:30 a.m. in room PLT-3775.