Differential evolution: a fast and simple numerical optimizer Export

@inproceedings{citeulike:5738709, abstract = {Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real-valued multi-modal functions. Function parameters are encoded as floating-point variables and mutated with a simple arithmetic operation. During mutation, a variable-length, one-way crossover operation splices perturbed best-so-far parameter values into existing population vectors. A novel sampling technique adaptively scales the step-size of perturbations as the population evolves. DE's selection criterion demands that improved vectors always be accepted. The performance of DE on a testbed of 15 functions is compared with a variety of recently published results encompassing many different methods. DE converged for all 15 functions and was the fastest method for solving 11 of them. DE's performance on the remaining 4 functions was competitive}, author = {Price, K. V.}, booktitle = {Fuzzy Information Processing Society, 1996. NAFIPS. 1996 Biennial Conference of the North American}, citeulike-article-id = {5738709}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/NAFIPS.1996.534790}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=534790}, doi = {10.1109/NAFIPS.1996.534790}, journal = {Fuzzy Information Processing Society, 1996. NAFIPS. 1996 Biennial Conference of the North American}, keywords = {rp}, pages = {524--527}, posted-at = {2009-09-08 04:10:52}, priority = {2}, title = {Differential evolution: a fast and simple numerical optimizer}, url = {http://dx.doi.org/10.1109/NAFIPS.1996.534790}, year = {1996} }

TY - CONF ID - citeulike:5738709 L3 - citeulike-article-id:5738709 N2 - Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real-valued multi-modal functions. Function parameters are encoded as floating-point variables and mutated with a simple arithmetic operation. During mutation, a variable-length, one-way crossover operation splices perturbed best-so-far parameter values into existing population vectors. A novel sampling technique adaptively scales the step-size of perturbations as the population evolves. DE's selection criterion demands that improved vectors always be accepted. The performance of DE on a testbed of 15 functions is compared with a variety of recently published results encompassing many different methods. DE converged for all 15 functions and was the fastest method for solving 11 of them. DE's performance on the remaining 4 functions was competitive JF - Fuzzy Information Processing Society, 1996. NAFIPS. 1996 Biennial Conference of the North American EP - 527 TI - Differential evolution: a fast and simple numerical optimizer T2 - Fuzzy Information Processing Society, 1996. NAFIPS. 1996 Biennial Conference of the North American SP - 524 KW - rp AU - Price, KV PY - 1996/// UR - http://dx.doi.org/10.1109/NAFIPS.1996.534790 ER -