Abstract
Based on an existing Genetic algorithm (GA) to TSP (IGT algorithm), by amending the original mapping operator and Inver-over operator and introducing differing operator for the first time, a new GA to TSP is proposed. Empirical results show that this new algorithm outperforms IGT algorithm in terms of solution quality, means and variances. Moreover, T-test exhibits the comprehensive advantage of the new algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Choi, I.-C., Kim, S.-I., Kim, H.-S.: A genetic algorithm with a mixed region search for the asymmetric traveling saleaman problem. Computers & Operations Research 30, 773–786 (2003)
Rego, C.: Relaxed tours and path ejections for the traveling saleaman problem. European Journal of Operational Research 106, 552–568 (1998)
Laporte, G.: The vehicle routing problem: an overview of exact and approximate algorithms. European Journal of Operational Reasearch 59, 345–358 (1992)
Chan, D., Mercier, D.: IC insertion: an application of the traveling salesman problem. International Journal of Production Research 27, 1837–1841 (1989)
Litke, J.: An improved solution to the traveling saleaman problem with thousands of nodes. Communications of the optimization (1985)
Zhi-Hua, C., Jin_Guo, P., Wei, G.: An Improved Envolutionary Algorithm for the Traveling Salesman Problem. Chinese Journal of Computers 28, 823–828 (2005)
Zheng-Jun, P., Li-Shan, K., Yu-Ping, C.: Evolutionary Computation. Tsinghua University Press,Guangxi Science and Technology Press, Beijing, Nanning (1988)
Tao, G., Michalewicz, Z.: Inver—over operator for the TSP. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 803–812. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, G., Li, C., Zhu, J., Li, Y., Liu, W. (2010). An Efficient Genetic Algorithm for the Traveling Salesman Problem. In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2010. Communications in Computer and Information Science, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16388-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-16388-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16387-6
Online ISBN: 978-3-642-16388-3
eBook Packages: Computer ScienceComputer Science (R0)