Name
Abstract | ||
|---|---|---|
This paper introduces the Extended Deterministic Local Search (EDLS) algorithm for Latin Hypercube (LH) designs. The main goal of the algorithm is to improve an existing algorithm towards a better uniformity of the data distribution, while maintaining a good computational performance. After presenting background information about LH designs and how to assess their quality (choice of loss function), the EDLS algorithm is explained and compared to two other algorithms for LH designs. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SSCI.2015.63 | 2015 IEEE Symposium Series on Computational Intelligence |
Keywords | Field | DocType |
extended deterministic local search algorithm,maximin Latin hypercube design,EDLS algorithm,LH design,data distribution,loss function | Approximation algorithm,Data modeling,Minimax,Mathematical optimization,Algorithm design,Local search (optimization),Hypercube,Latin hypercube sampling,Mathematics | Conference |
ISBN | Citations | PageRank |
978-1-4799-7560-0 | 1 | 0.35 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ebert, T. | 1 | 3 | 0.75 |
| torsten fischer | 2 | 1 | 0.35 |
| julian belz | 3 | 1 | 1.71 |
| tim oliver heinz | 4 | 1 | 0.35 |
| geritt kampmann | 5 | 1 | 0.35 |
| Oliver Nelles | 6 | 99 | 17.27 |