Multi-objective hyperparameter optimization approach with genetic algorithms towards efficient and environmentally friendly machine learning

This paper presents a multi-objective optimization approach for developing efficient and environmentally friendly Machine Learning models. The proposed approach uses Genetic Algorithms to simultaneously optimize the accuracy, time-to-solution, and energy consumption simultaneously. This solution proposed to be part of an Automated Machine Learning pipeline and focuses on architecture and hyperparameter search. A customized Genetic Algorithm scheme and operators were developed, and its feasibility was evaluated using the XGBoost ML algorithm for classification and regression tasks. The results demonstrate the effectiveness of the Genetic Algorithm for multi-objective optimization, indicating that it is possible to reduce energy consumption while minimizing predictive performance losses.