Data Structures and Algorithms

    

Speedup Genetic Algorithm Using Parallel Processing Method

Authors: Hak Kun Ri, Chol Hun Pak, Nam Song An

Genetic Algorithm (GA) is one of most popular swarm based evolutionary search algorithms that involve multiple data independent computations. Such computations can be made in parallel processing method on GPU cores using Compute Unified Design Architecture (CUDA) platform. In this paper, various operations of GA such as fitness evaluation, selection, crossover and mutation, etc. are implemented in parallel on GPU cores and then performance is compared with its serial implementation. Result shows that the overall computational time can substantially be decreased by parallel implementation on GPU cores. The proposed implementations resulted in 1.18 to 3.68 times faster than the corresponding serial implementation on CPU.

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[v1] 2024-05-07 21:07:59

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