Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
Classifier fitness based on accuracy
Evolutionary Computation
Tournament selection: stable fitness pressure in XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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
Learning classifier system ensemble for data mining
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Mining breast cancer data with XCS
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
Learning Classifier Systems
Observer-invariant histopathology using genetics-based machine learning
Natural Computing: an international journal
On the appropriateness of evolutionary rule learning algorithms for malware detection
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Performance evaluation of evolutionary algorithms in classification of biomedical datasets
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Speedup character-based matching in learning classifier systems with Xor
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Prediction using Pittsburgh learning classifier systems: APCS use case
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
IEEE Transactions on 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
Self-adaptation of learning rate in XCS working in noisy and dynamic environments
Computers in Human Behavior
Transparent, online image pattern classification using a learning classifier system
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Engineering Applications of Artificial Intelligence
International Journal of Applied Metaheuristic Computing
Particle swarm classification: A survey and positioning
Pattern Recognition
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Learning classifier systems: introducing the user-friendly textbook
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper compares performance of the Pittsburgh-style system GAssist with the Michigan-style system XCS on several datamining problems. Our analysis shows that both systems are suitable for datamining but have different advantages and disadvantages. The study does not only reveal important differences between the two systems but also suggests several structural properties of the underlying datasets.