Abstract
Genetic algorithm (GA) is a way of solving problems by mimicking the same processes mother nature uses, and has been widely used in many fields. However, it also has some limitations. In this paper, an improved GA is proposed for overcoming these limitations, which is based on the simulated annealing (SA) technology. In binary code, the disadvantageous of selecting crossover gene bit with equal probability is analyzed in depth. Based on these analysis, a crossover operator is proposed, whose crossover probability being adaptive changed with gene bits. The experimental results show that the proposed improved GA algorithm has greater convergence performance than classical GA.
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
References
Melanie, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Jin, C., Wang, S.H.: Robust watermark algorithm using genetic algorithm. J. Inf. Sci. Eng. 23(2), 661–670 (2007)
Šetinc, M., Gradišar, M., Tomat, L.: Optimization of a highway project planning using a modified genetic algorithm. Optimization 64(3), 687–707 (2015)
Moradi, M.H., Abedini, M.: A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int. J. Electr. Power Energy Syst. 34(1), 66–74 (2012)
Elhaddad, Y., Sallabi, O.: A new hybrid genetic and simulated annealing algorithm to solve the traveling salesman problem. World Congr. Eng. 1, 11–14 (2010)
Lombardi, A.M.: Estimation of the parameters of ETAS models by simulated annealing. Scientific reports, 5, Article number: 8417 (2015). doi:10.1038/srep08417
e Oliveira Jr., H.A.: Global optimization and its applications. In: Aguiar e Oliveira Junior, H., Ingber, L., Petraglia, A., Rembold Petraglia, M., Augusta Soares Machado, M. (eds.) Stochastic Global Optimization and Its Applications with Fuzzy Adaptive Simulated Annealing. ISRL, vol. 35, pp. 11–20. Springer, Heidelberg (2012)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)
Wang, C.F., Liu, K., Shen, P.P.: Hybrid artificial bee colony algorithm and particle swarm search for global optimization. Mathematical Problems in Engineering, Article ID 832949, 8 p. (2014)
Hwang, S.F., He, R.S.: A hybrid real-parameter genetic algorithm for function optimization. Adv. Eng. Inform. 20(1), 7–21 (2006)
Acknowledgments
This work was supported by Natural Social Science Foundation of China (Grant No.13BTQ050).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Jin, C., Liu, J. (2016). An Experimental Assessment of Hybrid Genetic-Simulated Annealing Algorithm. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_68
Download citation
DOI: https://doi.org/10.1007/978-3-319-40663-3_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40662-6
Online ISBN: 978-3-319-40663-3
eBook Packages: Computer ScienceComputer Science (R0)