AutoCIS at WCCI 2026

Automating Computational Intelligence Systems: Trends, Challenges, and Real-World Applications (5th Edition)

Aim and Scope

This special session focuses on the emerging challenges, advancements, and applications in automated algorithm design, configuration, and selection, particularly leveraging evolutionary computation techniques. As the demand for innovative solutions to complex, real-world problems grows, automated design frameworks have become increasingly critical in reducing development time and improving the quality of solutions across diverse domains such as optimisation, scheduling, healthcare, smart cities, transportation, and robotics.

A significant focus will be on technological advancements like Automated Machine Learning (AutoML), Neuroevolution, and Large Language Models (LLMs), which aim to automate the design of machine learning algorithms and neural network architectures. Additionally, the session will delve into the evolving field of hyper-heuristics, initially developed for combinatorial optimisation problems, and its impact on automating evolutionary techniques. Computational intelligence systems often employ genetic algorithms, fuzzy logic, neural networks, and multi-agent approaches, all of which require intricate design decisions and expert knowledge.

This session will address how evolutionary algorithms, such as genetic programming, are evolving to automate these processes and tackle complex real-world applications. Topics include exploring transfer learning, explainable artificial intelligence, landscape analysis, and integrating diverse computational techniques. The session aims to provide a platform for discussing theoretical frameworks, practical applications, and the future directions of automated algorithm design, moving beyond benchmark problems to address practical engineering challenges and real-world scenarios.

This Special Session aims to:

• Explore the state-of-the-art in automated algorithm design, configuration, and selection methodologies and frameworks.
• Address theoretical and practical challenges in the automation of algorithmic systems.
• Present innovative applications demonstrating the impact of automated design in solving complex real-world problems.
• Incentive for critical and technical discussions on the future of automated design, focusing on interdisciplinary collaboration.

Main Topics

• Automated hybridisation of intelligent techniques.
• Frameworks for automatic evolutionary algorithm design.
• Hyper-heuristics for metaheuristic composition and optimisation.
• AutoML applications in evolutionary computation.
• Genetic programming for automated algorithm design.
• Neuroevolution and transfer learning in algorithm automation.
• Reinforcement learning for adaptive algorithm design and configuration.
• Large Language Models for Automated Algorithm Design.
• Real-world applications of automated design systems.
• Challenges in the Theoretical and Practical Integration of Automated Systems.
• Other emerging topics in automated algorithm design, configuration, and selection.

Important Dates

Previous Editions

• 2025 IEEE CEC – Automating Computational Intelligence Systems (AutoCIS): Trends, Challenges, and Real-World Applications: https://tfaadcs.org/2024/11/27/autocis/
• 2024 IEEE WCCI/CEC – Automating Computational Intelligence Systems: Trends, Challenges, and Future Directions: https://www.hyper-heuristics.com/events/autocis/
• 2023 IEEE CEC – Automated Algorithm Design for Evolutionary Computation (AAD4EC): https://cs.ijs.si/autodesign4ec/
• 2023 IEEE CEC – Evolutionary Computation for Automated Algorithm Design (EC4AAD): https://www.hyper-heuristics.com/events/ec4aad23/
• 2022 IEEE WCCI/CEC – Automated Algorithm Design for Evolutionary Computation (AutoDesign4EC)

Organisers

Biographies

Jorge Cruz is a postdoctoral researcher (since 2025) at the University of Lille, CNRS, Inria, Centrale Lille, specializing in heuristic methods, automated algorithm design, and data science. Previously, he was a Research Professor (2021-2024) in the Research Group on Advanced Artificial Intelligence at the Tecnológico de Monterrey. He chairs the IEEE Computational Intelligence Society’s Task Force on Automated Algorithm Design, Configuration, and Selection. His research focuses on neuromorphic computing, hyper-heuristics, and metaheuristic optimization and their applications in complex problem-solving. He has contributed extensively to journals and conferences in his field. [>]

Tome Eftimov is a senior researcher at the Computer Systems Department of the Jožef Stefan Institute. He holds a PhD in Computer Science and has been a postdoctoral research fellow at Stanford University. Tome is vice-chair of the IEEE Computational Intelligence Society’s Task Force on Automated Algorithm Design, Configuration, and Selection. His research encompasses statistical data analysis, natural language processing, machine learning, and information theory. [>]

Nelishia Pillay, based at the University of Pretoria, South Africa, holds the Multichoice Joint-Chair in Machine Learning and the SARChI Chair in Artificial Intelligence for Sustainable Development. She leads multiple IEEE committees, including the Technical Committee on Intelligent Systems Applications. Her research focuses on hyper-heuristics, automated machine learning design, and combinatorial optimisation with applications in sustainable development. As the Nature-Inspired Computing Optimization Group (NICOG) founder, Prof. Pillay has made significant contributions to academic journals and international conferences. [>]