AI, Machine Learning and Optimization

Dear Colleagues,

Artificial intelligence (AI), machine learning (ML), and optimization techniques have revolutionized numerous fields in recent years, from healthcare and finance to manufacturing and transportation. The rapid advancement in computational capabilities has enabled increasingly complex AI and ML models, which in turn rely on sophisticated optimization methods to achieve their performance. Simultaneously, AI and ML approaches have enhanced traditional optimization techniques, leading to novel hybrid methods capable of efficiently finding high-quality solutions to computationally intractable problems and accelerating optimization processes for complex real-world applications.

This Special Issue on "AI, Machine Learning and Optimization" aims to bring together researchers and practitioners working at the intersection of these fields to share recent developments, innovative applications, and emerging trends. The interplay between AI, ML, and optimization presents rich opportunities for interdisciplinary research that can address pressing challenges across various domains. We welcome original research articles, comprehensive reviews, and case studies that showcase theoretical advances, algorithmic innovations, or novel applications in these areas.

Topics to be covered include the following:

• Deep learning architectures and optimization techniques.
• Reinforcement learning algorithms and applications.
• Metaheuristic and nature-inspired optimization methods.
• Explainable AI and interpretable machine learning models.
• Optimization methods for training neural networks.
• Multi-objective and constrained optimization.
• Evolutionary computation and genetic algorithms.
• Bayesian optimization and probabilistic methods.
• Graph neural networks and combinatorial optimization.
• Transfer learning and domain adaptation techniques.
• Time series forecasting and prediction models.
• AI and ML applications in operations research.
• Optimization under uncertainty and robust methods.
• Transformer models.
• Natural Language Processing.
• Approximation of NP-hard problems.
• Neural Architecture Search (NAS).
• Algorithm selection and configuration

Dr. John Abela
Prof. Dr. Wanquan Liu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.