Evolutionary Machine Learning for Real-World Applications

Dear Colleagues,

This Special Issue "Evolutionary Machine Learning for Real-world Applications" explores the integration of evolutionary computation (EC) techniques with machine learning (ML) to enhance performance, scalability, and adaptability in solving complex real-world challenges. Evolutionary computation methods have demonstrated significant potential in optimizing various aspects of machine learning, from hyperparameter tuning to model selection and feature engineering.

While machine learning has achieved remarkable success in diverse applications such as computer vision, natural language processing, biomedical analysis, and autonomous systems, traditional methods often struggle with large-scale, dynamic, and noisy data environments. Evolutionary computation provides a promising alternative by offering adaptive, self-optimizing, and robust learning strategies that can efficiently navigate complex search spaces.

This Special Issue aims to investigate both theoretical advancements and practical applications at the intersection of evolutionary computation and machine learning. We invite submissions on novel algorithms, hybrid techniques, and innovative applications of EC-driven machine learning across various domains. Topics of interest include, but are not limited to, the following:

• Evolutionary hyperparameter optimization;
• Evolutionary feature selection and dimensionality reduction;
• Neuroevolution for deep learning architectures;
• Surrogate models and evolutionary computation for machine learning;
• Evolutionary computation for symbolic regression and applications;
• Federated learning optimization for real-world applications;
• Evolutionary computer vision and image processing;
• Evolutionary transfer learning and transfer optimization;
• Large-scale evolutionary algorithms for machine learning;
• Evolutionary computation in the healthcare industry;
• Evolutionary algorithms for model selection and architecture search;
• Swarm intelligence in machine learning optimization;
• Multi-objective evolutionary optimization in machine learning;
• Co-evolutionary and hybrid evolutionary machine learning techniques;
• Automated machine learning (AutoML) with evolutionary strategies;
• Interpretability in evolutionary machine learning;
• Overfitting mitigation and robust validation in data-driven evolutionary computation.

We encourage original research contributions from the scientific community, including novel methodologies, comparative studies, and real-world case studies where evolutionary computation enhances machine learning applications.

We look forward to your contributions and to advancing the field through this Special Issue.

Dr. Azam Asilian Bidgoli
Guest Editor

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• evolutionary computation
• machine learning
• evolutionary machine learning
• swarm intelligence optimization
• multi-objective optimization
• neuroevolution
• deep learning optimization
• evolutionary architecture search