Abstract
Traveling salesman problem (TSP) is a classical NP-hard problem in combinational optimization. This paper adopted a novel genetic algorithm which adjust the crossover probability and mutation probability adaptively based on clustering and fuzzy system, and designed a new crossover operator to improve the performance of genetic algorithm (GA) for TSP. Experiments show that the proposed method is much better than the standard genetic algorithm with a higher convergent rate and success rate. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Wang, J., Huang, J., Rao, S., Xue, S., & Yin, J. (2008). An adaptive genetic algorithm for solving traveling salesman problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5227 LNAI, pp. 182–189). https://doi.org/10.1007/978-3-540-85984-0_23
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