Title :
Parallelizing a genetic operator for GPUs
Author :
Fujimoto, Naoki ; Tsutsui, Shigeyoshi
Author_Institution :
Osaka Prefecture Univ., Sakai, Japan
Abstract :
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.
Keywords :
genetic algorithms; graphics processing units; mathematical operators; parallel architectures; CUDA architecture; GPU program; NVIDIA GeForce GTX580 GPU; OX operator parallelization; TSP; genetic algorithms; genetic operator parallelization; order crossover operator parallelization; traveling salesman problem; Arrays; Cities and towns; Graphics processing units; Instruction sets; Sociology; Statistics; GPGPU; Parallel GA; many-thread programming;
Conference_Titel :
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location :
Cancun
Print_ISBN :
978-1-4799-0453-2
Electronic_ISBN :
978-1-4799-0452-5
DOI :
10.1109/CEC.2013.6557711