Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Standard and averaging reinforcement learning in XCS
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension
Evolutionary Computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Empirical analysis of generalization and learning in XCS with gradient descent
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Analysis and improvement of the genetic discovery component of XCS
International Journal of Hybrid Intelligent Systems - Data Mining and Hybrid Intelligent Systems
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning
IEEE Transactions on Evolutionary Computation
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Counter example for Q-bucket-brigade under prediction problem
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Fleet estimation for defence logistics using a multi-objective learning classifier system
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Self-adaptation of learning rate in XCS working in noisy and dynamic environments
Computers in Human Behavior
Self-adaptation of parameters in a learning classifier system ensemble machine
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Enhancing learning capabilities by XCS with best action mapping
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Learning classifier system with average reward reinforcement learning
Knowledge-Based Systems
XCS with adaptive action mapping
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Selection strategy for XCS with adaptive action mapping
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Improving the performance of the BioHEL learning classifier system
Expert Systems with Applications: An International Journal
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The accuracy-based XCS classifier system has been shown to solve typical data mining problems in a machine-learning competitive way. However, successful applications in multistep problems, modeled by a Markov decision process, were restricted to very small problems. Until now, the temporal difference learning technique in XCS was based on deterministic updates. However, since a prediction is actually generated by a set of rules in XCS and Learning Classifier Systems in general, gradient-based update methods are applicable. The extension of XCS to gradient-based update methods results in a classifier system that is more robust and more parameter independent, solving large and difficult maze problems reliably. Additionally, the extension to gradient methods highlights the relation of XCS to other function approximation methods in reinforcement learning.