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A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification

A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification

Nabil M. Hewahi, Enas Abu Hamra
Copyright: © 2017 |Volume: 10 |Issue: 3 |Pages: 21
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522511892|DOI: 10.4018/JITR.2017070104
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MLA

Hewahi, Nabil M., and Enas Abu Hamra. "A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification." JITR vol.10, no.3 2017: pp.48-68. https://doi.org/10.4018/JITR.2017070104

APA

Hewahi, N. M. & Abu Hamra, E. (2017). A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification. Journal of Information Technology Research (JITR), 10(3), 48-68. https://doi.org/10.4018/JITR.2017070104

Chicago

Hewahi, Nabil M., and Enas Abu Hamra. "A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification," Journal of Information Technology Research (JITR) 10, no.3: 48-68. https://doi.org/10.4018/JITR.2017070104

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Abstract

Artificial Neural Network (ANN) has played a significant role in many areas because of its ability to solve many complex problems that mathematical methods failed to solve. However, it has some shortcomings that lead it to stop working in some cases or decrease the result accuracy. In this research the authors propose a new approach combining particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to increase the classification accuracy of ANN. The proposed approach utilizes the advantages of both PSO and GA to overcome the local minima problem of ANN, which prevents ANN from improving the classification accuracy. The algorithms start with using backpropagation algorithm, then it keeps repeating applying GA followed by PSO until the optimum classification is reached. The proposed approach is domain independent and has been evaluated by applying it using nine datasets with various domains and characteristics. A comparative study has been performed between the authors' proposed approach and other previous approaches, the results show the superiority of our approach.

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