Name
Abstract | ||
|---|---|---|
We discuss the noisy optimisation problem, in which function evaluations are subject to random noise. Adaptation of pure random search to noisy optimisation by repeated sampling is considered. We introduce and exploit an improving bias condition on noise-affected pure random search algorithms. Two such algorithms are considered; we show that one requires infinite expected work to proceed, while the other is practical. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/s10898-004-9970-4 | J. Global Optimization |
Keywords | DocType | Volume |
sequential analysis,objective function,random search | Journal | 31 |
Issue | ISSN | Citations |
4 | 0925-5001 | 2 |
PageRank | References | Authors |
0.46 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David L. Alexander | 1 | 3 | 1.41 |
| David W. Bulger | 2 | 94 | 7.83 |
| James M. Calvin | 3 | 187 | 33.11 |
| H. Edwin Romeijn | 4 | 769 | 83.88 |
| Ryan L. Sherriff | 5 | 2 | 0.46 |