24th International Workshop on Learning Classifier Systems
(IWLCS 2021)

Program

IWLCS 2021 takes place on Sunday, 11 July, at 8:30–10:20 am (Lille's time zone, i.e. UTC/GMT+2 hours, Central European Summer Time). Please refer to the schedule on the GECCO website for the exact times of each presentations.

Welcome Note

An Overview of LCS Research from 2020 to 2021

An Experimental Comparison of Explore/Exploit Strategies for the Learning Classifier System XCS

A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies

General discussion

Aims and Scope of IWLCS

Learning Classifier Systems (LCSs) are a class of powerful Evolutionary Machine Learning (EML) algorithms that combine the global search of evolutionary algorithms with the local optimization of reinforcement or supervised learning. They form predictions by combining an evolving set of localized models each of which is responsible for a part of the problem space. While the localized models themselves are trained using machine learning techniques ranging from simple adaptive filters to more complex ones such as artificial neural networks, their responsibilities are optimized by powerful heuristic such as GAs.

Over the last four decades, LCSs have shown great potential in various problem domains such as behaviour modelling, online control, function approximation, classification, prediction and data mining. Their unique strengths lie in their adaptability and flexibility, them making only a minimal set of assumptions and, most importantly, their transparency. Topics that have been central to LCS for many years are more and more becoming a matter of high interest for other machine learning communities as well these days; the prime example is an increase in human interpretability of generated models which especially the booming Deep Learning community is keen on obtaining (Explainable AI). This workshop serves as a critical spotlight to disseminate the long experience of LCS research in these areas, to present new and ongoing research in the field, to attract new interest and to expose the machine learning community to an alternative, often advantageous, modelling paradigm. Particular topics of interest are (not exclusively):

• advances in LCS methods (local models, problem space partitioning, classifier mixing, …)

• evolutionary reinforcement learning (multi-step LCS, neuroevolution, …)

• state of the art analysis (quantitative/qualitative surveys, carefully crafted comparative experimental benchmarks, …)

• formal developments in LCSs (provably optimal parametrization, time bounds, generalization, …)

• interpretability of evolved knowledge bases (knowledge extraction, visualization, …)

• advances in LCS paradigms (Michigan/Pittsburgh style, hybrids, iterative rule learning, …)

• hyperparameter optimization (hyperparameter selection, online self-adaptation, …)

• applications (medical domains, bio-informatics, intelligence in games, cyber-physical systems, …)

• optimizations and parallel implementations (GPU acceleration, matching algorithms, …)

• other evolutionary rule-based ML systems (artificial immune/evolving fuzzy rule-based systems, …)

Program Committee

• Jaume Bacardit, Newcastle University, UK

• Lashon B. Booker, The MITRE Corporation, US

• Will N. Browne, Queensland University of Technology, Australia

• Larry Bull, The University of the West of England, UK

• Michael Heider, University of Augsburg, Germany

• Luis Miramontes Hercog, University of Notre Dame, US

• Karthik Kuber, Loblaw Companies Limited/York University, Canada, Canada

• Masaya Nakata, Yokohama National University, Japan

• Yusuke Nojima, Osaka Prefecture University, Japan

• David Pätzel, University of Augsburg, Germany

• Sonia Schulenburg, Level E Research, UK

• Shinichi Shirakawa, Yokohama National University, Japan

• Anthony Stein, University of Hohenheim, Germany

• Sven Tomforde, Kiel University, Germany

• Ryan J. Urbanowicz, University of Pennsylvania, US

• Danilo Vasconcellos Vargas, Kyushu University, Japan

• Alexander Wagner, University of Hohenheim, Germany