Automating Computational Intelligence Systems: Trends, Challenges, and Future Directions
This Special Session at WCCI-CEC 2024 will focus on the emerging trends and challenges in automating computational intelligence systems, a field crucial for solving complex problems across various industries, including data mining, transportation, health systems, and robotics. Computational intelligence systems, employing techniques like neural networks, fuzzy logic, genetic algorithms, and multi-agent approaches, require intricate design decisions and expert knowledge, making their development time-intensive.
A significant focus of our session will be on the advancements in Auto-Machine Learning and Neuroevolution, aimed at automating the design of machine learning algorithms and neural network architectures, respectively. We will delve into the evolving field of Hyper-Heuristics, initially developed for combinatorial optimization problems, and its effectiveness in automating metaheuristic techniques.
The session will address how these automated systems, primarily using evolutionary algorithms such as genetic programming, are evolving to tackle complex real-world applications. Key topics include exploring transfer learning, explainable artificial intelligence, and integrating different computational techniques. We aim to explore how these initiatives move beyond benchmark problems to address practical engineering challenges and real-world scenarios.
Our goal is to provide a platform for discussing recent developments in automated algorithm design, examining both the theoretical frameworks and practical applications. This session will serve as a forum for researchers and practitioners to share insights, discuss challenges, and explore future directions in the automation of computational intelligence systems.
Call for Papers
The topics of this special session include but are not limited to the following topics:
- Automated Hybridization of Intelligent Techniques
- Towards a Theoretical and Software Framework for Automatic Evolutionary Algorithm Design
- Automatic System Development Using Hyper-Heuristics
- Genetic Programming in Automatic Evolutionary Algorithm Design
- AutoML in Automatic Evolutionary Algorithm Design
- Evolutionary Algorithms for Tailoring and Tuning Automatic Algorithm Design Strategies
- Neuroevolution and Transfer Learning in Automatic Evolutionary Algorithm Design
- Reinforcement Learning in Automatic Evolutionary Algorithm Design
- Applications of Automatic Design Systems in Real-World Scenarios
- Hyper-Heuristics for Metaheuristic Composition Optimization Problems
- January 29, 2024 – Paper Submission Deadline
- March 15, 2024 – Paper Acceptance Notification
- May 1, 2024 – Camera-ready submission & Early Registration Deadline
- June 30 – July 5, 2024 – IEEE WCCI 2024
Jorge M. Cruz-Duarte
Jorge M. Cruz-Duarte is a Research Professor in the Research Group on Advanced Artificial Intelligence at the Tecnologico de Monterrey, Mexico. He is also a member of the Mexican National System of Researchers (SNI-CONACyT), IEEE, and the Mexican Academy of Computer Sciences (AMEXCOMP). Prof. Cruz-Duarte is also a reviewer of several scientific journals, including ASOC, SWEVO, ATE, IEEE Access, and Mathematical Reviews. His research interests include Automatic Design, Heuristic Methods, Fractional Calculus, Applied Thermodynamics, Data Science, and Artificial Intelligence.
Nelishia Pillay is a Professor at the University of Pretoria, South Africa, and holds the Multichoice Joint-Chair in Machine Learning and the SARChI Chair in Artificial Intelligence for Sustainable Development. She leads multiple committees and task forces within the IEEE, including the Technical Committee on Intelligent Systems Applications and the Task Force on Automated Algorithm Design. As an associate editor for several respected journals, her research focuses on hyper-heuristics, automated machine learning design, combinatorial optimization, and applications in sustainable development. Prof. Pillay founded the NICOG research group and has contributed extensively to journals and conferences in her expertise.
Rong Qu is a Professor at the University of Nottingham, UK, specializing in complex scheduling and optimization problems in logistics and personnel scheduling, using evolutionary computation, mathematical programming, and automated machine learning design. The Royal Society, UK, awarded her the 2022 Leverhulme Trust Senior Research Fellowship. Prof. Qu is an Associate Editor for six international journals, including IEEE Transactions on Evolutionary Computation. She has guest-edited special issues on automated algorithms and has co-founded a symposium series at IEEE SSCI. A Chair/Vice-Chair for IEEE Technical Committees and Task Forces, Prof. Qu has been an IEEE Senior Member since 2012.