Optimizing neural network training with Genetic Algorithms

Junen Chai

doi:10.54254/2755-2721/42/20230780

Research Article

Open access

Published on 23 February 2024

Download pdf

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

Export citation

Optimizing neural network training with Genetic Algorithms

Junen Chai *^,1,

¹ Ningbo Hanvos Kent School

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/42/20230780

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

View pdf

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.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

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

Conference website: https://2023.confmla.org/

ISBN：978-1-83558-309-8(Print) / 978-1-83558-310-4(Online)

Conference date: 18 October 2023

Editor：Mustafa İSTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.42

ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).