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Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network

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Abstract

In recent years, multi-objective evolutionary optimization algorithms have shown success in different areas of research. Due to their efficiency and power, many researchers have concentrated on adapting evolutionary algorithms to generate Pareto solutions. This paper proposes a new memetic adaptive multi-objective evolutionary algorithm that is based on a three-term backpropagation network (MAMOT). This algorithm is an automatic search method for optimizing the parameters and performance of neural networks, and it relies on the use of the adaptive non-dominated sorting genetic algorithm-II integrated with the backpropagation algorithm, being used as a local search method. The presented MAMOT employs a self-adaptive mechanism toward improving the performance of the proposed algorithm and a local optimizer improving all the individuals in a population in order to obtain better accuracy and connection weights. In addition, it selects an appropriate number of hidden nodes simultaneously. The proposed method was applied to 11 datasets representing pattern classification problems, including two-class, multi-class and complex data reflecting real problems. Experiments were performed, and the results indicated that the proposed method is viable in pattern classification tasks compared to a multi-objective genetic algorithm based on a three-term backpropagation network (MOGAT) and some of the methods mentioned in the literature. The statistical analysis results of the t test and Wilcoxon signed-ranks test also show that the performance of the proposed method is significantly better than MOGAT.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the editor for their useful advice and constructive comments.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Alzaiem Alazhari University, 13311, Khartoum North, Sudan

    Ashraf Osman Ibrahim

  2. Arab Open University, Khartoum, Sudan

    Ashraf Osman Ibrahim

  3. Faculty of computer science, Future University, Khartoum, Sudan

    Ashraf Osman Ibrahim

  4. UTM Big Data Centre, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Siti Mariyam Shamsuddin

  5. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA

    Ajith Abraham

  6. Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Sultan Noman Qasem

  7. Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz, Yemen

    Sultan Noman Qasem

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  1. Ashraf Osman Ibrahim
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  4. Sultan Noman Qasem
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Correspondence to Ashraf Osman Ibrahim.

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Ibrahim, A.O., Shamsuddin, S.M., Abraham, A. et al. Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network. Neural Comput & Applic 31, 4945–4962 (2019). https://doi.org/10.1007/s00521-018-03990-0

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