Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Classifier systems and genetic algorithms
Artificial Intelligence
Proceedings of the seventh international conference (1990) on Machine learning
Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Toward a Model of Intelligence as an Economy of Agents
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The anticipatory classifier system and genetic generalization
Natural Computing: an international journal
Classifier Systems and the Animat Problem
Machine Learning
Evolutionary Computation
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
Improving the Performance of Genetic Algorithms in Classifier Systems
Proceedings of the 1st International Conference on Genetic Algorithms
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An Algorithmic Description of ACS2
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Applications of Learning Classifier Systems
Applications of Learning Classifier Systems
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Classifier prediction based on tile coding
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Classifier fitness based on accuracy
Evolutionary Computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Clustering with XCS on Complex Structure Dataset
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Clustering with XCS and agglomerative rule merging
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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When Learning Classifier Systems (LCSs) were introduced by John H. Holland in the 1970s, the intention was the design of a highly adaptive cognitive system. Since then, LCSs came a long way. Interest strongly decreased in the late 80s and early 90s due the complex interactions of several learning mechanisms. However, since the introduction of the accuracy-based XCS classifier system by Stewart W. Wilson in 1995 and the modular analysis of several LCSs thereafter, interest re-gained momentum. Current research has shown that LCSs can effectively solve data-mining problems, reinforcement learning problems, other predictive problems, and cognitive control problems. Hereby, it was shown that performance is machine learning competitive, but learning is taking place online and is often more flexible and highly adaptive. Moreover, system knowledge can be easily extracted.The Learning Classifier System tutorial provides a gentle introduction to LCSs and their general functioning. It then surveys the current theoretical understanding of the systems and their proper application to various problem domains. Finally, we provide a suite of current successful LCS implementations and discuss the most promising areas for future research and applications.