We present a first evaluation of a new accuracy-based Pittsburghstyle learning classifier system (LCS) for supervised learning of multi-dimensional continuous decision problems: The SupRB-1 (Supervised Rule-Based) learning system. Designed primarily for finding parametrizations for industrial machinery, SupRB-1 learns an approximation of a continuous quality function from examples (consisting of situations, choices and associated qualities-all continous, the first two possibly multi-dimensional) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. This paper shows and discusses preliminary results of SupRB-1's performance on an additive manufacturing problem.
The International Workshop on Learning Classifier Systems (IWLCS) is an annual workshop at the GECCO conference where new concepts and results regarding learning classifier systems (LCSs) are presented and discussed. One recurring part of the workshop agenda is a presentation that reviews and summarizes the advances made in the field over the last year; this is intended to provide an easy entry point to the most recent progress and achievements. The 2020 and 2021 presentations were accompanied by survey workshop papers, a practice which we hereby continue. We give an overview of all the LCS-related publications from 11 March 2021 to 10 March 2022. The 42 publications we review are grouped into six overall topics: Formal theoretic advances, new LCS architectures, LCS-based reinforcement learning, algorithmic improvements to existing LCSs, combinations of LCS and Deep Learning models and, finally, a variety of applications of LCSs.
CCS CONCEPTS• Computing methodologies → Rule learning; Genetic algorithms; • General and reference → Surveys and overviews.
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