A Review on the Impact of Evolutionary Optimization Algorithms in Enhancing Learning Processes

Abstract

This article provides a comprehensive review of the impact of evolutionary optimization algorithms on improving learning processes. Evolutionary algorithms, inspired by natural evolution, offer powerful tools for solving complex optimization problems in machine learning. The review covers the theoretical foundations of these algorithms, including Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution, and explores their applications in enhancing learning models. The study compares the performance and effectiveness of these algorithms in various machine learning contexts, highlighting their strengths in optimizing parameters, feature selection, and model structure. Additionally, the review identifies challenges such as parameter tuning, computational complexity, and convergence to local optima, which can limit the effectiveness of these algorithms. The findings suggest that evolutionary optimization algorithms have significant potential to improve learning processes, but also underscore the need for further research to address existing gaps and refine these methods. This review concludes with recommendations for future research directions, emphasizing the importance of developing hybrid algorithms and improving parameter tuning methods to achieve better performance in complex and high-dimensional learning tasks.