Abstract
The Multi-layer Perceptron (MLP) is extensively used in solving real-world problems. Backpropagation (BP) is often employed to deal with training data in MLP. However, this method is quickly faced with premature convergence problems and local traps. In this study, the Hybrid Genetic-Bees Algorithms (HGBA) in training MLP is reported for the first time. The genetic algorithm (GA) is used to improve the global search phase of the Bees Algorithm (BA), which is then to search for better solutions. The proposed HGBA is tested on four standard UCI classification datasets with different levels of difficulty. Experimental results show that HGBA provides significantly better performance than Particle Swarm Optimization (PSO) in training MLP with higher accuracy.
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Anh, T.T.M., Vi, T.D. (2023). Hybrid Genetic-Bees Algorithm in Multi-layer Perceptron Optimization. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-19-6634-7_11
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