24th International Workshop on Learning Classifier Systems
(IWLCS 2021)
to be held entirely virtual as part of GECCO 2021 (The Genetic and Evolutionary Computation Conference), 10 to 14 July 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
David Pätzel, Alexander R. M. Wagner, Michael HeiderAn Overview of LCS Research from 2020 to 2021
David Pätzel, Michael Heider, Alexander R. M. WagnerAn Experimental Comparison of Explore/Exploit Strategies for the Learning Classifier System XCS
Tim Hansmeier, Marco PlatznerA Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies
Jordan T. Bishop, Marcus Gallagher, Will N. BrowneGeneral 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, …)
Deadlines
Submission deadline: 12 April 2021
Decisions due: 26 April 2021
Camera-ready version: 3 May 2021
Early and mandatory author registration deadline: 3 May 2021
Submission Information
Papers are expected to report on innovative ideas and novel research results around the topic of LCS and general Evolutionary Rule-based Machine Learning (ERBML). Reported results and findings have to be integrated with the current state of the art and should provide details and metrics allowing for an assessment of practical as well as statistical significance. Contributions bringing in novel ideas and concepts from related fields such as general ML and EC are explicitly solicited but authors are at the same time strongly encouraged to clearly state the relevance and relation to the field of LCS and ERBML.
Submissions must
conform to the GECCO submission instructions.
not exceed 8 pages, excluding references.
be submitted via GECCO’s submission system.
Organization Committee
David Pätzel is a PhD student 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 2015 and his M.Sc. in the same field in 2017. His main research is directed towards Learning Classifier Systems with a focus on developing a more formal, probabilistic understanding of LCSs such as XCS(F) that can, for example, be used to improve existing algorithms. Besides that, his research interests include evolutionary machine learning algorithms, reinforcement learning and pure functional programming. He is an elected organizing committee member of the International Workshop on Learning Classifier Systems since 2020.
Alexander Wagner is a PhD student at the Department of Artificial Intelligence in Agricultural Engineering at the University of Hohenheim, Germany. He received his B.Sc. and M.Sc. degrees in computer science from the University of Augsburg in 2018 and 2020, respectively. His bachelor’s thesis already dealt with the field of Learning Classifier Systems. This sparked his interest and he continued working on Learning Classifier Systems, especially XCS, during his master studies. Consequently, he also dedicated his master’s thesis to this topic in greater depth. His current research focuses on the application of Learning Classifier Systems, in particular XCS and its derivatives, to self-learning adaptive systems designed to operate in real-world environments, especially in agricultural domains. In this context, the emphasis of his research is to increase reliability of XCS or LCS in general. His research interests also include reinforcement learning, evolutionary machine learning algorithms, neural networks and neuro evolution.
Michael Heider is a PhD student 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).
Advisory Board
Jaume Bacardit, Newcastle University, UK
Will N. Browne, Victoria University of Wellington, New Zealand
Martin V. Butz, University of Tübingen, Germany
John Holmes, University of Pennsylvania, US
Muhammad Iqbal, Higher Colleges of Technology/Xtracta, United Arab Emirates
Pier Luca Lanzi, Politecnico Di Milano, Italy
Masaya Nakata, Yokohama National University, Japan
Kamran Shafi, University of New South Wales, Australia
Anthony Stein, University of Hohenheim, Germany
Wolfgang Stolzmann, CMORE Automotive, Germany
Ryan J. Urbanowicz, University of Pennsylvania, US
Stewart W. Wilson, Prediction Dynamics, US
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
Previous Editions of IWLCS
IWLCS@GECCO 2020 - https://iwlcs.organic-computing.de/2020-edition
IWLCS@GECCO 2019 - https://iwlcs.organic-computing.de/2019-edition
IWLCS@GECCO 2018 - http://itslab.inf.kyushu-u.ac.jp/~vargas/iwlcs_2018/
IWLCS@GECCO 2017 - http://itslab.inf.kyushu-u.ac.jp/~vargas/erbml_2017/
IWLCS@GECCO 2016 - http://www.cas.hc.uec.ac.jp/conferences/iwlcs2016/
IWLCS@GECCO 2014 - http://homepages.ecs.vuw.ac.nz/~iqbal/iwlcs2014/index.html
IWLCS@GECCO 2013 - http://homepages.ecs.vuw.ac.nz/~iqbal/iwlcs2013/index.html
IWLCS@GECCO 2012 - http://home.deib.polimi.it/loiacono/iwlcs2012/index.php?n=Main.HomePage
IWLCS@GECCO 2011 - http://home.deib.polimi.it/loiacono/iwlcs2011/