Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/8134
Title: Evolving neural networks using bird swarm algorithm for data classification and regression applications
Authors: Aljarah, Ibrahim 
Faris, Hossam 
Mirjalili, Seyedali 
Al-Madi, Nailah 
Sheta, Alaa 
Mafarja, Majdi 
Keywords: Computer algorithms;Neural networks (Computer science) - Optimization;Computer simulation;Wireless sensor networks;Computational intelligence;Bird Swarm Algorithm;Classification
Issue Date: 2019
Abstract: This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets.
URI: http://hdl.handle.net/20.500.11889/8134
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