A Survey on Learning Classifier Systems from 2022 to 2024

Abubakar Siddique, Michael Heider, Muhammad Iqbal, Hiroki Shiraishi

Abstract: Learning classifier systems (LCSs) are a state-of-the-art methodology for developing rule-based machine learning by applying discovery algorithms and learning components. LCSs have become proficient at linking environmental features to describe simple patterns in data. They have a natural ability to split a solution into niches. The decision-making process of an LCS-based system is interpretable, which is a step toward explainable AI. A broad range of LCS-based applications have been developed to solve real-world problems. The International Workshop on Learning Classifier Systems (IWLCS) is one of the pioneer and successful workshops at GECCO. 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 LCSs. This work follows the tradition of previous surveys at the workshop and provides an overview of the LCS-related publications from March 2022 to March 2024. Based on the nature of contributions, the publications selected for review are divided into the following five groups: (i) Theoretical and Architectural Enhancements, (ii) Explainability, (iii) Applications, (iv) Role of Metaheuristics in LCSs, and (v) Miscellaneous Contributions. This survey provides an easy entry point to the most recent progress and achievements in the field of LCSs.