A coarse-grained parallelization of genetic algorithms

(1) Muhamad Radzi Rathomi Mail (Department of Informatics, Universitas Maritim Raja Ali Haji, Indonesia)
(2) * Reza Pulungan Mail (Computer Science and Electronics Department, Universitas Gadjah Mada, Indonesia)
*corresponding author


Genetic algorithms are frequently used to solve optimization problems. Nowadays, the problems to be solved by genetic algo-rithms become increasingly complex and require large computa-tion times. One solution to speed up genetic algorithm processing is to use parallelization. In this paper, a new method to increase the speed of genetic algorithms in finding the optimal solutions by parallelizing the processing of subpopulations is proposed. The proposed parallelization method is coarse-grained and em-ploys two levels of parallelization: message passing with MPI and Single Instruction Multiple Threads with GPU. Experimental results show that the accuracy of the proposed parallel genetic algorithm is similar to the sequential genetic algorithm. Parallel-ization with coarse-grained method, however, can improve the processing speed of genetic algorithms. Convergence speed of the parallel genetic algorithm is also much better than the se-quential genetic algorithm.


Genetic algorithms; parallelization; coarse-grained; MPI ;GPU




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