Name
Abstract | ||
|---|---|---|
In evolutionary algorithms, the typical post-processing phase involves selection of the best-of-run individual, which becomes the final outcome of the evolutionary run. Trivial for deterministic problems, this task can get computationally demanding in noisy environments. A typical naive procedure used in practice is to repeat the evaluation of each individual for the fixed number of times and select the one with the highest average. In this paper, we consider several algorithms that can adaptively choose individuals to evaluate basing on the results evaluations which have already been performed. The procedures are designed without any specific assumption about noise distribution. In the experimental part, we compare our algorithms with the naive and optimal procedures, and find out that the performance of typically used naive algorithm is poor even for relatively moderate noise. We also show that one of our algorithms is nearly optimal for most of the examined situations. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1389095.1389278 | Proceedings of the 15th annual conference on Genetic and evolutionary computation |
Keywords | Field | DocType |
robustness,noise,evolutionary algorithms,evolutionary algorithm,evolutionary computation,evolutionary computing,uncertainty | Mathematical optimization,Evolutionary algorithm,Computer science,Evolutionary computation,Robustness (computer science),Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.42 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wojciech Jaskowski | 1 | 257 | 13.97 |
| Wojciech Kotlowski | 2 | 158 | 16.32 |