论文信息 - An Improved Genetic Algorithm to Solve the Traveling Salesman Problem

An Improved Genetic Algorithm to Solve the Traveling Salesman Problem

The Genetic Algorithm (GA) is one of the most important methods used to solve many combinatorial optimization problems. Therefore, many researchers have tried to improve the GA by using different methods and operations in order to find the optimal solution within reasonable time. This paper proposes an improved GA (IGA), where the new crossover operation, population reformulates operation, multi mutation operation, partial local optimal mutation operation, and rearrangement operation are used to solve the Traveling Salesman Problem. The proposed IGA was then compared with three GAs, which use different crossover operations and mutations. The results of this comparison show that the IGA can achieve better results for the solutions in a faster time. Keywords— AI, Genetic algorithms, TSP.

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[1] David B. Beasley,et al. An overview of genetic algorithms: Part 1 , 1993 .

[2] Pedro Larrañaga,et al. Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[3] Venansius Baryamureeba,et al. PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 8 , 2005 .

[4] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5] Sanghamitra Bandyopadhyay,et al. New Genetic Operators for Solving TSP: Application to Microarray Gene Ordering , 2005, PReMI.

[6] David Beasley,et al. An overview of genetic algorithms: Part 1 , 1993 .

[7] Sanghamitra Bandyopadhyay,et al. New operators of genetic algorithms for traveling salesman problem , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..