Parallel genetic algorithm on the CUDA architecture

Petr Pospichal, Jiri Jaros and Josef Schwarz
Brno University of Technology, Faculty of Information Technology, Department of Computer Systems, Bozetechova 2, 612 66 Brno, Czech Republic
Applications of Evolutionary Computation, Lecture Notes in Computer Science, 2010, Volume 6024/2010, 442-451


   title={Parallel Genetic Algorithm on the CUDA Architecture},

   author={Posp{‘i}chal, P. and Jaros, J. and Schwarz, J.},

   journal={Applications of Evolutionary Computation},





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This paper deals with the mapping of the parallel island-based genetic algorithm with unidirectional ring migrations to nVidia CUDA software model. The proposed mapping is tested using Rosenbrock’s, Griewank’s and Michalewicz’s benchmark functions. The obtained results indicate that our approach leads to speedups up to seven thousand times higher compared to one CPU thread while maintaining a reasonable results quality. This clearly shows that GPUs have a potential for acceleration of GAs and allow to solve much complex tasks.
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