Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Learning classifier system ensemble and compact rule set
Connection Science - Evolutionary Learning and Optimisation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Evolving Classifiers Ensembles with Heterogeneous Predictors
Learning Classifier Systems
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
Modeling social learning of language and skills
Artificial Life
Lecture notes in computer science: research on multi-robot avoidance collision planning based on XCS
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
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
EEM: evolutionary ensembles model for activity recognition in Smart Homes
Applied Intelligence
Engineering Applications of Artificial Intelligence
Classifier ensemble optimization for human activity recognition in smart homes
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Adaptive support framework for wisdom web of things
World Wide Web
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This paper presents an investigation into exploiting the population-based nature of learning classifier systems (LCSs) for their use within highly parallel systems. In particular, the use of simple payoff and accuracy-based LCSs within the ensemble machine approach is examined. Results indicate that inclusion of a rule migration mechanism inspired by parallel genetic algorithms is an effective way to improve learning speed in comparison to equivalent single systems. Presentation of a mechanism which exploits the underlying niche-based generalization mechanism of accuracy-based systems is then shown to further improve their performance, particularly, as task complexity increases. This is not found to be the case for payoff-based systems. Finally, considerably better than linear speedup is demonstrated with the accuracy-based systems on a version of the well-known Boolean logic benchmark task used throughout.