Evolutionary Optimization for Machine Learning - Hyperparameter Tuning

Abstract

Hyperparameter tuning is a critical step in the machine learning model development process, as it significantly impacts the performance and generalization of models. Traditional approaches to hyperparameter tuning, such as grid search and random search, can be computationally expensive and inefficient, especially for complex models and large datasets. Evolutionary optimization methods offer a promising alternative for hyperparameter tuning, leveraging principles inspired by natural evolution to efficiently search the hyperparameter space. This paper provides a comprehensive overview of evolutionary optimization techniques, including genetic algorithms, evolutionary strategies, and genetic programming, and their application to hyperparameter tuning in machine learning. We discuss the advantages and limitations of evolutionary optimization for hyperparameter tuning and present case studies and experimental results that demonstrate the effectiveness of these methods in improving model performance. Finally, we highlight future research directions and challenges in the use of evolutionary optimization for hyperparameter tuning in machine learning.