Abstract
TSP(Traveling Salesman Problem) used widely for solving the optimization is the problem to find out the shortest distance out of possible courses where one starts a certain city, visits every city among N cities and turns back to a staring city. At this time, the condition is to visit N cities exactly only once. TSP is defined easily, but as the number of visiting cities increases, the calculation rate increases geometrically. This is why TSP is classified into NP-Hard Problem. Genetic Algorithm is used representatively to solve the TSP. Various operators have been developed and studied until now for solving the TSP more effectively. This paper applied the new Population Initialization Method (using the Random Initialization method and Induced Initialization method simultaneously), solved TSP more effectively, and proved the improvement of capability by comparing this new method with existing methods.
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
Jin gang gou.: Genetic Algorithm and the application, kousa (2000)
Goldberg, D.: Genetic Algorithms in search, Optimization, and Machine Learning. Addison Wesley, Reading (1989)
Boese, K.D.: Cost Versus Distanc In the Traveling Salesman Problem, Technical Report CSD-950018, UCLA (1995)
Mun byoung ro.: Genetic Algorithm, duyangsa (2003)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)
Grefenstette, J., Gopal, R., Rosmaita, B., Gucht, D.: Genetic Algorithms for the Traveling Salesman Problem. In: Proc. the 1st Inter. Conf. on GAs and Their Applications (1985)
Whitley, D., Starkweather, T., Fuquay, D.: Scheduling problems and traveling salesman: the genetic edge recombination and operator. In: Proc. Third Int. Conf. G As, pp. 133–140 (1989)
Whitley, D., Starkweather, T., Fuquay, D.: Scheduling Problems and the Traveling Salesman: the Genetic Edge Recombination Operator. In: Proc. Third Int. Conf. on Genetic Algorithms and Their Applications (1989)
Ting, C.-K.: Improving Edge Recombination through Alternate Inheritance and Greedy Manner. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 210–219. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, RG., Jung, CY. (2006). The Improved Initialization Method of Genetic Algorithm for Solving the Optimization Problem. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_87
Download citation
DOI: https://doi.org/10.1007/11893295_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
eBook Packages: Computer ScienceComputer Science (R0)