A coarse-grained parallelization of genetic algorithms

Muhamad Radzi Rathomi (Department of Informatics, Universitas Maritim Raja Ali Haji)
Reza Pulungan (Computer Science and Electronics Department, Universitas Gadjah Mada)

International Journal of Advances in Intelligent Informatics
Vol 4, No 1 (2018): March 2018

Genetic algorithms are frequently used to solve optimization problems. However, the problems become increasingly complex and time consuming. One solution to speed up the genetic algorithm processing is to use parallelization. The proposed parallelization method is coarse-grained and employs two levels of parallelization: message passing with MPI and Single Instruction Multiple Threads with GPU. Experimental results show that the accuracy of the proposed approach is similar to the sequential genetic algorithm. Parallelization with coarse-grained method, however, can improve the processing and convergence speed of genetic algorithms.