Parallelizing a genetic operator for GPUs
Noriyuki Fujimoto, Shigeyoshi Tsutsui · 2013
Genetic algorithms (GAs) have parallelism among applications of genetic operators to individuals, but in order to extract high performance of a GPU, parallelizing each genetic operator is desirable. This paper presents parallelization of the OX (order crossover) operator and experimentally show that our parallelized OX is effective on a GPU based on the CUDA architecture. The experiments with an NVIDIA GeForce GTX580 GPU show that our GPU program for the traveling salesman problem (TSP) is about up to 101.3 times faster than the corresponding CPU program on a single core of 2.67 GHz Intel Xeon X5550.