ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Iterative prototype optimisation with evolved improvement steps
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Black-box optimization benchmarking of two variants of the POEMS algorithm on the noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison of cauchy EDA and pPOEMS algorithms on the BBOB noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Experimental comparison of six population-based algorithms for continuous black box optimization
Evolutionary Computation
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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.