Evolutionary Machine Learning

Evolutionary machine learning (EML) leverages evolutionary algorithms to optimize various aspects of machine learning, from model architecture and hyperparameter tuning to data preprocessing and fairness considerations. Current research emphasizes applying EML to diverse problems, including few-shot learning, automated machine learning (AutoML) pipeline design, and the development of explainable AI models, often employing genetic programming, particle swarm optimization, and other evolutionary strategies. This interdisciplinary field is significantly impacting various domains, improving the efficiency, interpretability, and performance of machine learning models across applications such as drug discovery, materials science, and large language model analysis.