ICTACT Journal on Soft Computing

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Genetic Algorithm on General Purpose Graphics Processing Unit: Parallelism Review


Affiliations
1 Department of Information Technology, Walchand College of Engineering, India
2 Department of Computer Engineering, Pune University, India
3 Department of Computer Science and Engineering, Walchand College of Engineering, India
     

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Genetic Algorithm (GA) is effective and robust method for solving many optimization problems. However, it may take more runs (iterations) and time to get optimal solution. The execution time to find the optimal solution also depends upon the niching-technique applied to evolving population. This paper provides the information about how various authors, researchers, scientists have implemented GA on GPGPU (General purpose Graphics Processing Units) with and without parallelism. Many problems have been solved on GPGPU using GA. GA is easy to parallelize because of its SIMD nature and therefore can be implemented well on GPGPU. Thus, speedup can definitely be achieved if bottleneck in GAs are identified and implemented effectively on GPGPU. Paper gives review of various applications solved using GAs on GPGPU with the future scope in the area of optimization.

Keywords

Genetic Algorithm (GA), Parallel Genetic Algorithm (PGA), General Purpose Graphics Processing Unit (GPGPU), Compute Unified Device Architecture (CUDA), Open Computing Language (OpenCL).
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  • Genetic Algorithm on General Purpose Graphics Processing Unit: Parallelism Review

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Authors

A. J. Umbarkar
Department of Information Technology, Walchand College of Engineering, India
M. S. Joshi
Department of Computer Engineering, Pune University, India
N. M. Rothe
Department of Computer Science and Engineering, Walchand College of Engineering, India

Abstract


Genetic Algorithm (GA) is effective and robust method for solving many optimization problems. However, it may take more runs (iterations) and time to get optimal solution. The execution time to find the optimal solution also depends upon the niching-technique applied to evolving population. This paper provides the information about how various authors, researchers, scientists have implemented GA on GPGPU (General purpose Graphics Processing Units) with and without parallelism. Many problems have been solved on GPGPU using GA. GA is easy to parallelize because of its SIMD nature and therefore can be implemented well on GPGPU. Thus, speedup can definitely be achieved if bottleneck in GAs are identified and implemented effectively on GPGPU. Paper gives review of various applications solved using GAs on GPGPU with the future scope in the area of optimization.

Keywords


Genetic Algorithm (GA), Parallel Genetic Algorithm (PGA), General Purpose Graphics Processing Unit (GPGPU), Compute Unified Device Architecture (CUDA), Open Computing Language (OpenCL).