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Authors
Affiliations
1 Department of Information Technology, Walchand College of Engineering, IN
2 Department of Computer Engineering, Pune University, IN
3 Department of Computer Science and Engineering, Walchand College of Engineering, IN
Source
ICTACT Journal on Soft Computing, Vol 3, No 2 (2013), Pagination: 492-497
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).
