27th International Workshop on

Evolutionary Rule-based
Machine Learning

IWERL 2024 (formerly iwlcs)

to be held as part of GECCO 2024 (The Genetic and Evolutionary Computation Conference), 14 to 18 July 2024 in Melbourne, Australia (online participation possible).

Download our printable call for papers here!

Aims and Scope of IWERL

Modern machine learning systems offer significant potential for addressing real-world challenges. However, a notable limitation of the majority of these systems is their ``black-box'' nature. The decision-making process of these models is often difficult to interpret, making it challenging for users to understand how a model arrived at a particular decision. The interpretability of decisions is critical in many real-world applications such as defense, biomedical, and lawsuits. Moreover, many modern systems require extensive memory, huge computational resources, and enormous training data, which can be resource-intensive and hinder their widespread adoption.  

Evolutionary rule-based machine learning (ERL) stands out for its ability to provide interpretable decisions. The majority of ERL systems generate niche-based solutions, require less memory, and can be trained using comparatively small data sets. A key factor that makes these models interpretable is the generation of human-readable rules. Consequently, the decision-making process of the ERL systems is interpretable, which is an important step toward eXplainable AI (XAI).

The International Workshop on Evolutionary Rule-based Machine Learning (IWERL), previously known as the International Workshop on Learning Classifier Systems (IWLCS), stands as a cornerstone within the vibrant history of GECCO. Celebrating its 27th edition, IWERL is one of the pioneer and successful workshops at GECCO. This workshop plays an important role in nurturing the future of evolutionary rule-based machine learning. It serves as a beacon for the next generation of researchers, inspiring them to delve deep into evolutionary rule-based machine learning, with a particular focus on Learning Classifier Systems (LCSs).

ERL represents a collection of machine learning techniques that leverage the strengths of various metaheuristics to find an optimal set of rules to solve a problem. These methods have been developed using a diverse array of learning paradigms, including supervised learning, unsupervised learning, and reinforcement learning. ERL encompasses several prominent categories, such as Learning Classifier Systems, Ant-Miner, artificial immune systems, and fuzzy rule-based systems. The modes or model structures of these systems are optimized using evolutionary, symbolic, or swarm-based methods. The hallmark characteristic of the ERL models is their innate comprehensibility, which encompasses traits like explainability, transparency, and interpretability. This property has garnered significant attention within the machine learning community, aligning with the broader interest of Explainable AI.

This workshop is designed to provide a platform for sharing the research trends in the realm of ERL. It aims to highlight modern implementations of ERL systems for real-world applications and to show the effectiveness of ERL in creating flexible and eXplainable AI systems. Moreover, this workshop seeks to attract new interest in this alternative and often advantageous modelling paradigm.

Topics of interest include but are not limited to:


Submission deadline: 8 April 2024

Decisions due: 3 May 2024

Camera-ready version: 10 May 2024

Mandatory author registration deadline: as camera-ready

Why submit to IWERL ’24?

Your submission will

Submission Information

This workshop accepts two types of submissions:

Submissions must

Workshop Organization

Michael Heider

University of Augsburg (DE)


Michael Heider is a doctoral candidate at the Department of Computer Science at the University of Augsburg, Germany. He received his B.Sc. in Computer Science from the University of Augsburg in 2016 and his M.Sc. in Computer Science and Information-oriented Business Management in 2018. His main research is directed towards Learning Classifier Systems, especially following the Pittsburgh style, with a focus on regression tasks encountered in industrial settings. Those have a high regard for both accurate as well as comprehensive  solutions. To achieve comprehensibility he focuses on compact and simple rule sets. Besides that, his research interest include optimization techniques and unsupervised learning (e.g. for data augmentation or feature extraction). He is an elected organizing committee member of the International Workshop on Learning Classifier Systems since 2021.

Abubakar Siddique

Wellington Institute of Technology, Te Pūkenga – Whitireia WelTec (NZ)


Dr. Siddique's main research lies in creating novel machine learning systems, inspired by the principles of cognitive neuroscience, to provide efficient and scalable solutions for challenging and complex problems in different domains, such as Boolean, computer vision, navigation and Bioinformatics. He has provided a tutorial on Learning Classifier Systems: Cognitive inspired machine learning for eXplainable AI at GECCO 2022. He is engaged as an author and reviewer for different journals and international conferences including IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, IEEE Computational Intelligence Magazine, GECCO, IEEE CEC and EuroGP.

Dr. Siddique did his Bachelor's in Computer Science from Quaid-i-Azam University, Master's in Computer Engineering from U.E.T Taxila and Ph.D. in Computer Engineering from Victoria University of Wellington. He was the recipient of the VUWSA Gold Award and the “Student Of The Session” award during his Ph.D. and bachelor studies, respectively. He spent nine years at Elixir Pakistan, a California (USA) based leading software company. His last designation was a Principal Software Engineer where he led a team of software developers. He developed enterprise-level software for customers such as Xerox, IBM and Adobe. Currently, he is a lecturer at the Wellington Institute of Technology and a Postdoctoral Research Fellow at Victoria University of Wellington.

Hiroki Shiraishi

Yokohama National University  (JP)


Hiroki Shiraishi is a doctoral candidate in the Department of Electrical Engineering and Computer Science at Yokohama National University, Yokohama, Japan. He received his B.Eng. and M.Eng. degrees in informatics from the University of Electro-Communications, Tokyo, Japan, in 2021 and 2023, respectively. Between 2023 and 2024, he was a visiting student at the Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China. His research areas include evolutionary computation, evolutionary rule-based machine learning, neural networks, and fuzzy theory, particularly emphasizing Learning Classifier Systems (LCSs) and Learning Fuzzy-Classifier Systems (LFCSs). His seminal research in these areas earned him the Best Paper Award at GECCO 2022 in the Evolutionary Machine Learning track for his work on LCSs and a nomination for the Best Paper Award at GECCO 2023 for his advancements in LFCSs. 

Muhammad Iqbal

Higher Colleges of Technology (UAE)


Dr Iqbal has more than 20 years teaching and research experience of computer science. His main research interests are in the area of computational intelligence. His research focuses on pattern recognition and document recognition problem domains using computational intelligence and transfer learning techniques. Currently, Dr Iqbal is serving as Assistant Professor at Higher Colleges of Technology in United Arab Emirates. Previously, he was leading the research and development team at 'Xtracta Limited, New Zealand' to develop artificial intelligent powered information extraction systems capable of extracting data from scanned, photographed or digital documents with a focus on the financial domain. He has been serving as peer reviewer for international journals and conferences. To date he has authored more than 40 international publications, including top journal publications in IEEE Transactions on Evolutionary Computation, Evolutionary Computation, and Pattern Recognition, and two best paper awards at the Genetic and Evolutionary Computation Conference in 2013 and 2014 in the Evolutionary Machine Learning track. 

Advisory Board

PRELIMINARY Program Committee