NEA: Revolutionizing Vehicle Classification with Optimal Optimization through Neuro-Evolutionary Algorithm

Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced various fields, including intelligent transportation systems (ITS). A notable area of progress is vehicle classification, crucial for improving road safety and traffic management. Traditional vehicle classification methods often struggle with accuracy and efficiency in complex conditions. This study introduces a novel method using Neuro-Evolutionary Algorithms (NEAs) to optimize vehicle classification, combining neural networks with evolutionary computation for robust framework design and parameter optimization. NEAs adaptively refine neural network architectures, particularly enhancing their performance in diverse driving scenarios. Implemented in Google Colab, our NEAoptimized models demonstrated a remarkable classification accuracy of $98.35 \%$, outperforming traditional and contemporary methods. This approach not only advances vehicle classification accuracy but also sets the stage for future developments in ITS, promoting safer, more efficient mobility.