PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
An Efficient Fine-grained Parallel Genetic Algorithm Based on GPU-Accelerated
NPC '07 Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops
Parallel genetic algorithms on programmable graphics hardware
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Accelerating analysis of void space in porous materials on multicore and GPU platforms
International Journal of High Performance Computing Applications
Two ports of a full evolutionary algorithm onto GPGPU
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Hi-index | 0.00 |
A parallel solution to the implementation of evolutionary algorithms is proposed, where the most costly part of the whole evolutionary algorithm computations (the population evaluation), is deported to a GPGPU card. Experiments are presented for two benchmark examples on two models of GPGPU cards: first a "toy" problem is used to illustrate some noticable behaviour characteristics before a real problem is tested out. Results show a speed-up of up to 100 times compared to an execution on a standard micro-processor. To our knowledge, this solution is the first showing such an efficiency with GPGPU cards. Finally, the EASEA language and its compiler are also extended to allow users to easily specify and generate efficient parallel implementations of evolutionay algorithms using GPGPU cards.