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Research on the Parallel Implementation of Genetic Algorithm on CUDA Platform

Zuocheng Xing

Open publisher page 7 citations

Abstract

The CUDA technology provides conveniences of general computation for programmers,but there is no application programming interface of generating random number on CUDA.Therefore,this paper presents and implements a method for parallel producing random number algorithm on CUDA,and the methods is proved feasible by testing.On this condition,we implement a parallel implementation of GA on GPU,optimize the efficiency and precision of the standard GA,analyze the influence of population size and generations of evolution to efficiency and accuracy of this algorithm.The experiment shows that compared with GA Toolbox of MATLAB,the performance and the precision of this method is better.

About this research paper

What this paper is about

The CUDA technology provides conveniences of general computation for programmers,but there is no application programming interface of generating random number on CUDA.Therefore,this paper presents and implements a method for parallel producing random number algorithm on CUDA,and the methods is proved feasible by testing.On this condition,we implement a parallel implementation of GA on GPU,optimize the efficiency and precision of the standard GA,analyze the influence of population size and generations of evolution to efficiency and accuracy of this algorithm.The experiment shows that compared with GA Toolbox of MATLAB,the performance and the precision of this method is better.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

The CUDA technology provides conveniences of general computation for programmers,but there is no application programming interface of generating random number on CUDA.Therefore,this paper presents and implements a method for parallel producing random number algorithm on CUDA,and the methods is proved feasible by testing.On this condition,we implement a parallel implementation of GA on GPU,optimize the efficiency and precision of the standard GA,analyze the influence of population size and generations of evolution to efficiency and accuracy of this algorithm.The experiment shows that compared with GA Toolbox of MATLAB,the performance and the precision of this method is better.

Key concepts: CUDA, Computer science, Parallel computing, MATLAB, Computation, Algorithm, Genetic algorithm, General-purpose computing on graphics processing units

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