Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1569901.1570101 | GECCO |
Keywords | Field | DocType |
stochastic sampling,random permutation,evolutionary method,space-filling property,random number,low-discrepancy sequence,evolutionary algorithm,efficient sampling,better space-filling property,complex space,evolutionary algorithms,combinatorial optimization | Halton sequence,Mathematical optimization,Evolutionary algorithm,Computer science,Permutation,Combinatorial optimization,Sampling (statistics),Complex space,Hypercube | Conference |
Citations | PageRank | References |
2 | 0.39 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| François-Michel De Rainville | 1 | 199 | 9.27 |
| Christian Gagné | 2 | 627 | 52.38 |
| Olivier Teytaud | 3 | 794 | 84.86 |
| Denis Laurendeau | 4 | 803 | 169.72 |