BibTeX Entry

@inproceedings{kubalik2009c,
  category = {ida-publications},
  author = {Kubalik J},
  title = {Black-box optimization benchmarking of prototype optimization with evolved improvement steps for noiseless function testbed},
  booktitle = {GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference},
  year = {2009},
  publisher = {ACM},
  address = {New York, NY, USA},
  pages = {2303--2308},
  url = {http://portal.acm.org/citation.cfm?id=1570321#},
  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 10^5 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.},
  vvvs = {0},
}