Abstract
Genetic Algorithm (GA) is a powerful tool for science computing, while Parallel Genetic Algorithm (PGA) further promotes the performance of computing. However, the traditional parallel computing environment is very difficult to set up, much less the price. This gives rise to the appearance of moving dense computing to graphics hardware, which is inexpensive and more powerful. The paper presents a hierarchical parallel genetic algorithm, implemented by NVIDIA’s Compute Unified Device Architecture (CUDA). Mixed with master-slave parallelization method and multiple-demes parallelization method, this algorithm has contributed to better utilization of threads and high-speed shared memory in CUDA.
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Zhang, S., He, Z. (2009). Implementation of Parallel Genetic Algorithm Based on CUDA. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_4
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DOI: https://doi.org/10.1007/978-3-642-04843-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04842-5
Online ISBN: 978-3-642-04843-2
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