Parallelizing a genetic operator for GPUs

Author

Fujimoto, Naoki ; Tsutsui, Shigeyoshi

Author_Institution

Osaka Prefecture Univ., Sakai, Japan

fYear

2013

fDate

20-23 June 2013

Firstpage

1271

Lastpage

1277

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CEC.2013.6557711

Filename

6557711