Optimizing neural network training with Genetic Algorithms

Research Article
Open access

Optimizing neural network training with Genetic Algorithms

Junen Chai 1*
  • 1 Ningbo Hanvos Kent School    
  • *corresponding author chaijunen@hanvos-kent.com
Published on 23 February 2024 | https://doi.org/10.54254/2755-2721/42/20230780
ACE Vol.42
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-309-8
ISBN (Online): 978-1-83558-310-4

Abstract

In modern society, computer plays an important role among all human beings. Through the increasing development of technology, some problems happened gradually. In order to solve and regenerate the country, individuals should test their strengths. This paper discusses how to use genetic algorithms to optimize neural network training. As an important tool of machine learning, neural networks have made remarkable achievements in dealing with complex tasks. However, the training process of neural networks involves a lot of hyperparameter adjustment and weight optimization, which often requires a lot of time and computing resources. In order to improve the efficiency and performance of neural network training, humans should introduce genetic algorithms as an optimization method. Experiments are conducted on several common datasets to compare the performance of neural network training with Genetic Algorithm optimization against the traditional method. The results indicate that using Genetic Algorithms significantly improves the convergence speed and performance of neural networks while reducing the time and effort spent on hyperparameter tuning. Neural networks optimized using the Genetic Algorithm outperform their counterparts trained under the same time frame.

Keywords:

Hyperparameter, Optimization, Convergence, Neuro

Chai,J. (2024). Optimizing neural network training with Genetic Algorithms. Applied and Computational Engineering,42,220-224.
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References

[1]. Eshelman, L. J. The CHC Adaptive Search Algorithm: How to Have Safe. Search When Engaging in Nontraditional Genetic Recombination. Foundations of Genetic Algorithms, 1991: 265-283.

[2]. Goldberg, D. E. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. 1989: 95-99.

[3]. Yao, Xin. Evolving artificial neural networks. Proceedings of the IEEE, 87(9), 1999: 1423-1447.

[4]. Haupt, R. L., & Haupt, S. E. Practical genetic algorithms. John Wiley & Sons, 2004: 187-200.

[5]. Stanley, K. O., & Milkkulainen, R. Evolving neural networks through augmenting topologies. Evolutionary computation, 10(2), 2002: 99-127.

[6]. Youssef, A. M., & Azar, A. T. A Comprehensive Review of Genetic Algorithms and Their Applications in Cloud Computing. IEEE Access, 7, 2019: 74012-74032.

[7]. Deb, K., & Agrawal, S. Understanding interactions among genetic algorithm parameters. Proceedings of the 3rd Annual Conference on Evolutionary Programming, 1995: 350-365.

[8]. Stanley, K. O., & Mikkulainen, R. Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research, 21, 2004: 63-100.

[9]. Whitley, D., Rana, S., & Heckendorn, R. B. The island model genetic algorithm: On separability, population size and convergence. Journal of computational intelligence in finance, 2(4), 1993: 37-50.

[10]. Goldberg, D. E., & Richardson, J. Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the Second International Conference on Genetic Algorithms on including Parallelism, Local Search and Implicit Operators, 1987: 41-49.

Cite this article

Chai,J. (2024). Optimizing neural network training with Genetic Algorithms. Applied and Computational Engineering,42,220-224.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-309-8(Print) / 978-1-83558-310-4(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.42
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Eshelman, L. J. The CHC Adaptive Search Algorithm: How to Have Safe. Search When Engaging in Nontraditional Genetic Recombination. Foundations of Genetic Algorithms, 1991: 265-283.

[2]. Goldberg, D. E. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. 1989: 95-99.

[3]. Yao, Xin. Evolving artificial neural networks. Proceedings of the IEEE, 87(9), 1999: 1423-1447.

[4]. Haupt, R. L., & Haupt, S. E. Practical genetic algorithms. John Wiley & Sons, 2004: 187-200.

[5]. Stanley, K. O., & Milkkulainen, R. Evolving neural networks through augmenting topologies. Evolutionary computation, 10(2), 2002: 99-127.

[6]. Youssef, A. M., & Azar, A. T. A Comprehensive Review of Genetic Algorithms and Their Applications in Cloud Computing. IEEE Access, 7, 2019: 74012-74032.

[7]. Deb, K., & Agrawal, S. Understanding interactions among genetic algorithm parameters. Proceedings of the 3rd Annual Conference on Evolutionary Programming, 1995: 350-365.

[8]. Stanley, K. O., & Mikkulainen, R. Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research, 21, 2004: 63-100.

[9]. Whitley, D., Rana, S., & Heckendorn, R. B. The island model genetic algorithm: On separability, population size and convergence. Journal of computational intelligence in finance, 2(4), 1993: 37-50.

[10]. Goldberg, D. E., & Richardson, J. Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the Second International Conference on Genetic Algorithms on including Parallelism, Local Search and Implicit Operators, 1987: 41-49.

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