Abstract
Although the search process of GA may appear the global optimal solution, it can not guarantee that it is converged to the global optimal solution every time, but also the possibility of precocious defects occurs. For disadvantages of genetic algorithm, an improved adaptive GA is proposed with a real-coded, temporary memory set strategy, the improved cross-strategy and the improved mutation strategy. The results of demonstrate examples are proved that effectiveness of the improved GA is best.
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
Huiren, Z., Wansheng, T., Ben, N.: Optimization of multiple traveling salesman problem based on hierarchical genetic algorithm. Application Research of Computers 26(10), 3754–3755 (2009)
Ding-li, L.: Summary of the genetic algorithms. Science and Technology of West China 8(25), 41 (2009) (in Chinese)
Yee, L., Gao, Y.: Degree of population diversity——A perspective on premature convergence in gas and it’s Markov chain analysis. IEEE Trans. on NNs 8(5), 1132–1140 (1997)
Hongcheng, T.: Fault diagnosis technology and application based on artificial immune intelligent. Logistic Engineering University (2004) (in Chinese)
Ming, Z., Shulin, S.: Principle and application of Genetic Algorithms, Beijing, China (1999) (in Chinese)
Burnet, F.M.: The clonal selection theory of acquired immunity, London(1959)
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithm. IEEE Transactions on System, Man and Cybernatics 24(4), 656–667 (1994)
Strassner, T., Busold, M., Herrmann, W.A.: MM3 parametrization of four-and five-coordinated rhenium complexes by a genetic algorithm. Journal of Computational Chemistry (23), 282–290 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Hongcheng, T. (2012). An Improved Adaptive Genetic Algorithm. In: Tan, H. (eds) Knowledge Discovery and Data Mining. Advances in Intelligent and Soft Computing, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27708-5_99
Download citation
DOI: https://doi.org/10.1007/978-3-642-27708-5_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27707-8
Online ISBN: 978-3-642-27708-5
eBook Packages: EngineeringEngineering (R0)