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.
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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
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DOI: https://doi.org/10.1007/978-3-642-10684-2_14
Publisher Name: Springer, Berlin, Heidelberg
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