Evolutionary Rule-based
Machine Learning

David Pätzel 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 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 that can, for example, be used to improve existing algorithms. Besides that, his research interests include reinforcement learning, evolutionary machine learning algorithms 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 doctoral candidate 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. He is an elected organizing committee member of the International Workshop on Learning Classifier Systems since 2021.

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.

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.