Combining Machine Learning and Metaheuristics for Optimizing Complex Intelligent Systems
• 大类 : 计算机科学 - 1区
• 小类 : 计算机：信息系统 - 1区
With advancements in different domains, intelligent systems have become increasingly complex. It is challenging to ensure that these systems are resilient and adaptable in uncertain and dynamic environments. To design intelligent systems in such environments, integrating machine learning and metaheuristic algorithms provides a strong foundation. This approach has significantly improved the efficiency of intelligent systems, leading to more impactful solutions. This special issue seeks contributions that explore the convergence of machine learning and metaheuristics, with a focus on optimizing complex intelligent systems. We encourage submissions that cover novel algorithmic developments, comparative studies, real-world case analyses, and theoretical advancements at the intersection of these two methodologies.
Prof. Jian Wang (Executive Guest Editor)
China University of Petroleum (East China), China
Areas of Expertise: computational intelligence, machine learning, pattern recognition, deep learning, differential programming, clustering, fuzzy systems, and evolutionary computation
Assoc. Prof. Chanjuan Liu
Dalian University of Technology, China
Areas of Expertise: Intelligent Decision Making and Optimization
Prof. Jacek Mańdziuk
Warsaw University of Technology
Areas of Expertise: application of Computational Intelligence and Artificial Intelligence methods to games, dynamic and bilevel optimization problems, and human-machine cooperation in problem solving
Special issue information:
Topics of Interest
The following areas are of particular interest, although not limited to:
Parameter and structure optimization of machine learning models based on metaheuristicalgorithms.
Solution initialization, selection and generation of metaheuristics, and parameter optimization of MHs using data-driven insights from ML.
The integrated solution of MHs and ML contributes to designing more flexible, robust, and adaptive intelligent systems.
Benchmark Datasets and Evaluation Metrics: Contributions focused on developing new benchmark datasets and evaluation metrics tailored to ML and MHs.