Name
Title | ||
|---|---|---|
Black-box optimization benchmarking of prototype optimization with evolved improvement steps for noiseless function testbed |
Abstract | ||
|---|---|---|
This paper presents benchmarking of a stochastic local search algorithm called Prototype Optimization with Evolved Improvement Steps (POEMS) on the noise-free BBOB 2009 testbed. Experiments for 2, 3, 5, 10 and 20 D were done, where D denotes the search space dimension. The maximum number of function evaluations is chosen as 105 x D. Experimental results show that POEMS performs best on all separable functions and the attractive sector function. It works also quite well on multi-modal functions with lower dimensions. On the other hand, the algorithm fails to solve functions with high conditioning. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1570256.1570321 | genetic and evolutionary computation conference |
Keywords | DocType | Citations |
attractive sector function,stochastic local search algorithm,multi-modal function,high conditioning,prototype optimization,black-box optimization,improvement step,separable function,function evaluation,evolutionary computation,benchmarking,search space dimension,evolved improvement,stochastic local search,search space,evolutionary computing | Conference | 4 |
PageRank | References | Authors |
0.61 | 3 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiří Kubalik | 1 | 14 | 2.57 |