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Evolutionary Metaheuristics for Neural Network Optimization
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Author(s): Sridevi Tharanidharan (Applied College for Girls, King Khalid University, Mahala, Saudi Arabia), Prasanalakshmi Balaji (College of Computer Science, King Khalid University, Saudi Arabia), Gabriel Xiao-Guang Yue (European University Cyprus, Nicosia, Cyprus)and Renuka Devi (Presidency University, Bangalore, India)
Copyright: 2026
Pages: 26
Source title:
Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Source Author(s)/Editor(s): Prasanalakshmi Balaji (King Khalid University, Saudi Arabia), K. Martin Sagayam (Karunya Institute of Technology and Sciences, India), Aditi Sharma (Symbiosis International University, India)and Korhen Cengiz (University of Fujairah, UAE)
DOI: 10.4018/979-8-3373-0523-3.ch004
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Abstract
Neural networks have numerous hyperparameters, network designs, and weight combinations, making optimization challenging. Traditional methods like grid search and gradient descent often produce suboptimal solutions due to limited exploration. Genetic Algorithms (GAs), inspired by natural selection, provide an alternative by evolving populations across generations. Key GA operators include selection (e.g., Roulette Wheel, Tournament), crossover (e.g., Single-Point, Multi-Point), and mutation (e.g., Bit-Flip, Gaussian), which maintain diversity and avoid premature convergence. This chapter explores the use of GAs in neural network optimization, focusing on weight evolution, architectural search, and hyperparameter tuning. It also discusses GAs in medical image processing, improving tasks like disease detection, feature selection, and segmentation. GAs enhance diagnostic accuracy and treatment planning, especially in medical image analysis, offering effective optimization solutions in neural network training.
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