Heuristic Optimization and Machine Learning, 2nd Edition
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 April 2027 | Viewed by 23
Special Issue Editor
Interests: heuristic optimization; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in generative artificial intelligence have transformed how researchers generate code, analyze data, and interact with knowledge. Yet many of the most important scientific and engineering challenges still depend on optimization: the ability to search large solution spaces, satisfy constraints, balance competing objectives, and make robust decisions under uncertainty. Rather than reducing the importance of optimization, the rise of generative AI has made it even more central.
In this context, the interaction between machine learning and heuristic optimization is becoming increasingly important. Machine learning can enhance optimization through adaptive control, surrogate modeling, algorithm selection, and data-driven guidance, while optimization remains essential for improving intelligent systems and supporting effective action beyond prediction alone.
This Special Issue welcomes original research and review articles on recent developments at the intersection of heuristic optimization and machine learning, including (but not limited to) the following:
- Machine learning techniques for improving heuristic and metaheuristic optimization;
- Reinforcement learning for adaptive search, restart strategies, and parameter control;
- Surrogate-assisted and data-driven optimization;
- Large language models and generative AI for optimization and algorithm design;
- Machine learning for automatic algorithm selection and configuration;
- Neural combinatorial optimization;
- Hybrid optimization–learning frameworks;
- Multiobjective and many-objective optimization with learning components;
- Representation learning applied to optimization landscapes and search dynamics;
- Learnheuristics, meta-learning, and transfer learning in optimization;
- Explainability, robustness, and trustworthiness in intelligent optimization;
- Transfer of approaches between machine learning and optimization;
- Analysis of heuristic optimization using machine learning methods;
- Real-world applications in engineering, logistics, health, finance, energy, and scientific discovery.
Dr. Antonio Bolufé-Röhler
Guest Editor
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Keywords
- heuristic optimization
- metaheuristics
- machine learning
- deep learning
- reinforcement learning
- neuro-evolution
- learnheuristics
- evolutionary algorithms
- global optimization
- combinatorial optimization
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