Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
C4.5: programs for machine learning
C4.5: programs for 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
A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool
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
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
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
Genetic Programming and Evolvable Machines
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
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Despite several recent successful comparisons and applications of the accuracy-based learning classifier system XCS, it is hardly understood how crucial parameters should be set in XCS nor how XCS can be expect to scale up in larger problems. Previous research identified a covering challenge in XCS that needs to be obeyed to ensure that the genetic learning process takes place. Furthermore, a schema challenge was identified that, once obeyed, ensures the existence of accurate classifiers. This paper departs from these challenges deriving a reproductive opportunity bound. The bound assures that more accurate classifiers get a chance for reproduction. The relation to the previous bounds as well as to the specificity pressure in XCS are discussed as well. The derived bound shows that XCS scales in a machine learning competitive way.