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}, }