Abstract
This paper proposes a differential evolution (DE) algorithm that combines the strengths of multiple strategies together. The selection of strategy and control parameters for each individual happens every learning period. Thus the user gains the benefits of different strategies without difficult fine tuning of control parameters. The performance of the proposed MDE algorithm is evaluated on well-known benchmark functions and is superior to some other efficient and widely used variants of DE. In addition, MDE is applied to optimize both weights and biases of a single multiplicative neuron for prediction of DJIA with 3228 samples. Experiments show its better performance than other methods in learning ability and generalization.
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
Storn, R., Price, K.: Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. of Global Optimization 11, 341–359 (1997)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series). Springer, New York (2005)
Chakraborty, U.K. (ed.): Advances in Differential Evolution. Springer, Heidelberg (2008)
Fan, H.Y., Lampinen, J.: A trigonometric mutation operation to differential evolution. Journal of Global Optimization 27, 105–129 (2003)
Yadav, R.N., Kalra, P.K., John, J.: Time series prediction with single multiplicative neuron model. Applied Soft Computing 7, 1157–1163 (2007)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential Evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comp. 13, 398–417 (2009)
Brest, J., Greiner, S., et al.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evol. Comp. 10, 646–657 (2006)
Yadav, A., Mishra, D., Yadav, R.N., Ray, S., Kalra, P.K.: Time-series prediction with single integrate-and-fire neuron. Applied Soft Computing 7, 739–745 (2007)
Zhao, L., Yang, Y.: PSO-based single multiplicative neuron model for time series prediction. Expert Systems with Applications 36, 2805–2812 (2009)
Majhi, R., Panda, G., Majhi, B., Sahoo, G.: Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Systems with Applications 36, 10097–10104 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Worasucheep, C., Chongstitvatana, P. (2009). A Multi-strategy Differential Evolution Algorithm for Financial Prediction with Single Multiplicative Neuron. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-10684-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10682-8
Online ISBN: 978-3-642-10684-2
eBook Packages: Computer ScienceComputer Science (R0)