Parallel multi-objective evolutionary algorithms on graphics processing units Export

@inproceedings{citeulike:6072723, abstract = {Most real-life optimization problems or decision-making problems are multi-objective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Multi-Objective Evolutionary Algorithms (MOEAs) have been gaining increasing attention among researchers and practitioners. However, they may execute for a long time for some difficult problems, because several evaluations must be performed. Moreover, the non-dominance checking and the non-dominated selection procedures are also very time consuming. From our experiments, more than 99\% of the execution time is used in performing the two procedures. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose a parallel MOEA on consumer-level Graphics Processing Units (GPU). We perform many experiments on two-objective and three-objective benchmark problems to compare our parallel MOEA with a sequential MOEA and demonstrate that the former is much more efficient than the latter.}, address = {New York, NY, USA}, author = {Wong, Man L.}, booktitle = {GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference}, citeulike-article-id = {6072723}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1570256.1570354}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1570256.1570354}, doi = {10.1145/1570256.1570354}, isbn = {978-1-60558-505-5}, keywords = {2009, cuda, ga, genetic-algorithm, gpu, multi-objective, optimization}, location = {Montreal, Qu\'{e}bec, Canada}, pages = {2515--2522}, posted-at = {2009-11-05 09:15:43}, priority = {0}, publisher = {ACM}, title = {Parallel multi-objective evolutionary algorithms on graphics processing units}, url = {http://dx.doi.org/10.1145/1570256.1570354}, year = {2009} }

TY - CONF ID - citeulike:6072723 L3 - citeulike-article-id:6072723 N2 - Most real-life optimization problems or decision-making problems are multi-objective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Multi-Objective Evolutionary Algorithms (MOEAs) have been gaining increasing attention among researchers and practitioners. However, they may execute for a long time for some difficult problems, because several evaluations must be performed. Moreover, the non-dominance checking and the non-dominated selection procedures are also very time consuming. From our experiments, more than 99% of the execution time is used in performing the two procedures. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose a parallel MOEA on consumer-level Graphics Processing Units (GPU). We perform many experiments on two-objective and three-objective benchmark problems to compare our parallel MOEA with a sequential MOEA and demonstrate that the former is much more efficient than the latter. CY - Montreal, Qu\'{e}bec, Canada SN - 978-1-60558-505-5 AD - New York, NY, USA EP - 2522 TI - Parallel multi-objective evolutionary algorithms on graphics processing units PB - ACM T2 - GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference SP - 2515 KW - 2009 KW - cuda KW - ga KW - genetic-algorithm KW - gpu KW - multi-objective KW - optimization AU - Wong, Man PY - 2009/// UR - http://dx.doi.org/10.1145/1570256.1570354 ER -