This Special Session is organized within IEEE Congress on Evolutionary Computation (CEC) as part of the 2026 IEEE World Congress on Computational Intelligence (WCCI 2026).

Abstract: Modern expert systems increasingly utilize data of high complexity, uncertainty, and heterogeneity. The structure of this data has a significant impact on the learning, inference, and ultimately decision-making processes. In recent years, there has been growing interest in metaheuristic methods for data-level optimization, encompassing the selection, generation, cleansing, and adaptive transformation of data. Metaheuristics are increasingly playing the role of intelligent regulators of information flow in expert systems, enabling automatic tuning of data quality and representation to the specific problem. Consequently, data-level metaheuristic optimization is emerging as a crucial component in the development of modern, reliable, and interpretable expert systems.

Aims and Scope: This session seeks to unite researchers and practitioners who employ metaheuristic techniques (such as evolutionary algorithms, swarm intelligence, nature-inspired methods, and herd-based approaches) for data preparation in expert systems, machine learning, and symbolic-numerical artificial intelligence. The session will particularly focus on combining domain knowledge with adaptive optimization approaches to enhance the interpretability and resilience of expert systems operating in uncertain and evolving environments. Topics of interest include, but are not limited to:

• Metaheuristic feature selection and reduction for expert systems;
• Evolutionary approaches to class balancing and data reconstruction;
• Optimization of knowledge base parameters and rules in fuzzy and neuro-fuzzy systems;
• Metaheuristics for processing uncertain, incomplete, and approximate data;
• Adaptive exploration and exploitation strategies for input data optimization;
• Hybrid methods combining metaheuristics with deep learning and symbolic-numerical learning;
• Explainability and transparency of data optimization processes in the context of expert systems.

Detailed submission instructions and system information will be provided at a later time.

Important Dates

• Paper submission deadline 31 January 2026: (23:59, anywhere on Earth, i.e. UTC-12)
• Notification: 15 March 2026
• Camera-ready: 15 April 2026
• Conference: 21–26 June 2026

Dr. Mariusz Pleszczyński received the M.Sc. degree in mathematics and the Ph.D. degree in applied sciences, in the area of computer science, from the Czestochowa University of Technology, Czestochowa, Poland, in 2001 and 2009, respectively. He is an Adjunct Professor and Vice-Dean for Student Affairs and Education at the Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland. He has authored and co-authored more than 30 research papers published in international journals and conference proceedings in the area of applied computing. His current research interests include numerical methods, applied mathematics, graph neural networks, data processing, and computer tomography.

Katarzyna Prokop graduated with a Master's degree in Mathematics in 2023 and a Bachelor's degree in Computer Science in 2024, both from the Faculty of Applied Mathematics of the Silesian University of Technology. She is currently pursuing PhD in Computer Science. She also serves as the Dean’s Representative for Students with Disabilities and is a member of the Team for Class Schedule Management. Her research interests focus on image processing, with particular emphasis on image segmentation and deep learning applications. She combines solid mathematical foundations with computer science knowledge in her work, developing algorithms and models supporting advanced solutions in computer vision and data analysis.

Prof. Stefania Tomasiello has been an associate professor of computer science (SSD INF/01 in Italy) at the University of Salerno since October 2023. Responsible for the course Fuzzy Logic and Soft Computing (6 ECTS) at the University of Tartu. She was formerly an Associate Professor of Intelligent Systems at the University of Tartu (Institute of Computer Science, Chair of Data Science, Machine Learning Group) and previously, a permanent researcher with CO.RI.SA. (Research Consortium on Agent Systems), University of Salerno. Work-package leader in several funded projects and expert evaluator (ex-ante and ex-post) of applied research projects for the Italian Ministry of Economic Development and, formerly, the Italian Ministry of University and Research. She has been the principal investigator for the University of Tartu in the ERA-NET project “ConnectFarms”. She serves several indexed journals as an associate editor, such as IEEE Transactions on Neural Networks and Learning Systems, a premier journal in the field. She is Co-Editor in Chief of Evolutionary Intelligence (Springer, WoS, Scopus indexed). Senior IEEE member and senior INNS member. INNS Board of Governors 2025-2027. Currently supervising three PhD students. Italian scientific qualification (ASN) as full professor of computer science.

Dr. Anu Kaushik is currently serving as an Assistant Professor in the Department of Computer Science and Engineering at Chandigarh University, India. She has been actively involved in teaching, research, and academic coordination, contributing significantly to both undergraduate and postgraduate programs. She also serves as the Master Subject Coordinator and Examination Coordinator in the Department of CSE, where she plays a key role in curriculum planning and academic management. Her research interests include Vehicular Named Data Networks (VNDN), Connected and Autonomous Vehicles, and the Internet of Things (IoT). Dr. Kaushik has presented her research work at several national and international conferences and has contributed to the advancement of emerging technologies in vehicular communication and intelligent transport systems. In addition to her academic and research pursuits, Dr. Kaushik has also been involved in organizing IEEE-sponsored conferences and technical events, fostering collaboration and innovation within the research community. She continues to engage in scholarly activities aimed at integrating cutting-edge technologies with real-world applications in vehicular and IoT ecosystems.