Abstract
The Artificial bee colony (ABC) algorithm has been used in several optimization problems, including the optimization of synaptic weights from an Artificial Neural Network (ANN). However, it is easy to trap in local minimum and not enough to generate a robust ANN. Modular neural networks (MNNs) are especially efficient for certain classes of regression and classification problems, as compared to the conventional monolithic artificial neural networks. In this paper, we present a model of MNN based on ABC algorithm (ABC-MNN). The experiments show that, compared to the monolithic ABCNN model, classifier designed in this model has higher training accuracy and generalization performance.
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Ling, WX., Wang, YX. (2013). Using Modular Neural Network with Artificial Bee Colony Algorithm for Classification. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_47
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DOI: https://doi.org/10.1007/978-3-642-38703-6_47
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