Abstract
This study intends to present a hybrid evolutionary algorithm for sales forecasting problem. The proposed algorithm is a hybrid of particle swarm optimization (PSO) algorithm and genetic algorithm (GA) for gathering both their merits to improve the learning performance of radial basis function neural network (RBFnn). Model evaluation results of papaya milk sales data show that the proposed algorithm outperforms the sole approach algorithms and traditional Box–Jenkins model in accuracy.
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Kuo, R.J., Hu, TL., Chen, ZY. (2009). Sales Forecasting Using an Evolutionary Algorithm Based Radial Basis Function Neural Network. In: Yang, J., Ginige, A., Mayr, H.C., Kutsche, RD. (eds) Information Systems: Modeling, Development, and Integration. UNISCON 2009. Lecture Notes in Business Information Processing, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01112-2_8
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DOI: https://doi.org/10.1007/978-3-642-01112-2_8
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