Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Extracted global structure makes local building block processing effective in XCS
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Analysis of the initialization stage of a Pittsburgh approach learning classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An abstraction agorithm for genetics-based reinforcement learning
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
DXCS: an XCS system for distributed data mining
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An autonomous explore/exploit strategy
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Smart crossover operator with multiple parents for a Pittsburgh learning classifier system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
Meta-optimizing semantic evolutionary search
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
A Learning Classifier System Approach to Relational Reinforcement Learning
Learning Classifier Systems
Intrusion detection with evolutionary learning classifier systems
Natural Computing: an international journal
The multi-label OCS with a genetic algorithm for rule discovery: implementation and first results
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An adaptive genetic-based signature learning system for intrusion detection
Expert Systems with Applications: An International Journal
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
A self-organized, distributed, and adaptive rule-based induction system
IEEE Transactions on Neural Networks
The class imbalance problem in UCS classifier system: a preliminary study
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
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Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with animal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in different problem types, problem structures, concept spaces, and hypothesis spaces stayed nearly unpredictable. This thesis has the following three major objectives: (1) to establish a facetwise theory approach for LCSs that promotes system analysis, understanding, and design; (2) to analyze, evaluate, and enhance the XCS classifier system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the interactive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more advanced LCSs including Holland's originally envisioned cognitive systems.