vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff8a770e0000001124020001000500 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.
A.J. Umbarkar1, M.S. Joshi2 and N.M. Rothe3
1,3Walchand College of Engineering, India,2Pune University, India
Genetic Algorithm (GA), Parallel Genetic Algorithm (PGA), General Purpose Graphics Processing Unit (GPGPU), Compute Unified Device Architecture (CUDA), Open Computing
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 3 , Issue: 2 , Pages: 492-497 )
Date of Publication :
January 2013
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127
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