CUDA programming applied to constrained TSP using a genetic algorithm

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In this project, we explore the potential promise of parallel computing on a graphic processing unit (GPU) using the CUDA parallel computing platform and programming model for parallel metaheuristics for combinatorial optimization problems.


Our test problem and metaheuristic is the quintessential NP-Hard problem, the Traveling Salesman Problem (TSP), and commonly used genetic algorithm.

The specific TSP variant explored is the precedence constrained TSP. Our problem and the heuristic employed are intended to provide a way to explore the potential of this parallel computing platform.






[1] Y. Yun, C. Moon, Genetic algorithm approach for precedence-constrained sequencing problems, Springer Science and Business Media, 29 July 2009 pg. 379 – 388.
[2] C. Moon, J. Kim, G. Choi, Y. Seo, An efficient algorithm for the traveling salesman problem with precedence constraints, European Journal of Operational Research 140, 2002, pg. 606-617.
[3] E. L. Lawler, J.K. Lenstra, A.H.G. Rinnooy, D.B. Shmoys, Traveling salesman problem: a guided tour of combinatorial optimization, 1985.
[4] D. Goldberg, Genetic algorithms in search, optimization and machine learning, 1989.

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1 review for CUDA programming applied to constrained TSP using a genetic algorithm

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    I think your site will help a lot of students to do their projects better.

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