Modern machine learning systems, including generative AI and large language models (LLMs), offer significant potential for addressing real-world challenges across a wide range of applications. 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 in biomedicine, autonomous vehicles, law, finance, and critical infrastructure. 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 and knowledge encodings. 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 29th 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:

• Advances in ERL methods: local models, problem space partitioning, rule mixing, …
• Applications of ERL: medical, navigation, bioinformatics, computer vision, games, cyber-physical systems, …
• State-of-the-art analysis: surveys, sound comparative experimental benchmarks, carefully crafted reproducibility studies, …
• Formal developments in ERL: provably optimal parametrization, time bounds, generalization, …
• Comprehensibility of evolved rule sets: knowledge extraction, visualization, interpretation of decisions, eXplainable AI, …
• Advances in ERL paradigms: Michigan/Pittsburgh style, hybrids, iterative rule learning, …
• Hyperparameter optimization for ERL: hyperparameter selection, online self-adaptation, …
• Optimizations and parallel implementations: GPU acceleration, matching algorithms, …
• Generative AI and LLMs in ERL: integrating generative models and large language models for rule generation, natural language explanations, enhanced interpretability, …