Abstract
Genetic algorithm is an evolutionary algorithm. It is particularly suitable for solving NP-complete optimization problems. In this paper, we propose a rapid genetic algorithm based on chaos mechanism. We introduce the chaos mechanism into the genetic algorithm. Using the ergodic property of the chaos movement, this method can remedy the defect of premature convergence in the genetic algorithm. In the search, this method continuously compresses the searching intervals of the optimization variable for increasing convergence speed. Experiments indicate that this method is a rapid and effective evolutionary 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.
Similar content being viewed by others
References
Cobb, H.G.: An Investigation into the Use of Hyper mutation as an Adaptive Operator in Genetic Algorithms Having Continuous, Time-Dependent Nonstationary Environments, Navy Center for Applied Research in Artificial Intelligence, 1990, 6760 (NLR Memorandum), Washington, D.C. (1990)
Ursem, R.K.: When Sharing Fails. In: Proceedings of the 2001 Congress on Evolutionary Computation, CEC 2001, pp. 873–879 (2001)
Leung, Y.W., Wang, Y.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transaction on Evolutionary Computation 5(1), 41–53 (2001)
Liao, G.-C., Tsao, T.-P.: Application embedded chaos search immune genetic algorithm for short-term unit commitment. Electric Power Systems Research 7(2), 135–144 (2004)
Coelho, L.S., Mariani, V.C.: Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Transactions on Power Systems 21(2), 989–996 (2006)
Juang, C.F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems Man and Cybernetics, Part B 34, 997–1006 (2004)
El-Mihoub, T., Hopgood, A., Nolle, L., Battersby, A.: Performance of hybrid genetic algorithms incorporating local search. In: Horton, G. (ed.) 18th European Simulation Multiconference (ESM 2004), Magdeburg, Germany, pp. 154–160 (2004)
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
Gao, J., Xiao, M., Zhang, W. (2010). A Rapid Chaos Genetic Algorithm. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-13495-1_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13494-4
Online ISBN: 978-3-642-13495-1
eBook Packages: Computer ScienceComputer Science (R0)