Abstract
Genetic algorithm is a widely used tool for generating searching solutions in NP-hard problems. The genetic algorithmon a particular problem should be specifically designed for parallelization and its performance gain might vary according to the parallelism hidden within the algorithm. NVIDIA GPUs that support the CUDA programming paradigm provide many processing units and a shared address space to ease the parallelization process. A heuristic genetic algorithm on the traveling salesman problem is specially designed to run on CPU. Then a corresponding CUDA program is developed for performance comparison. The experimental results indicate that a sequential genetic algorithm with intensive interactions can be accelerated by being translated into CUDA code for GPU execution.
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© 2011 Springer-Verlag Berlin Heidelberg
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Chen, S., Davis, S., Jiang, H., Novobilski, A. (2011). CUDA-Based Genetic Algorithm on Traveling Salesman Problem. In: Lee, R. (eds) Computer and Information Science 2011. Studies in Computational Intelligence, vol 364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21378-6_19
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DOI: https://doi.org/10.1007/978-3-642-21378-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21377-9
Online ISBN: 978-3-642-21378-6
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