An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Generalization in Wilson's Classifier System
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Strength or Accuracy? Fitness Calculation in Learning Classifier Systems
Learning Classifier Systems, From Foundations to Applications
Classifier fitness based on accuracy
Evolutionary Computation
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS
Learning Classifier Systems
A first assessment of the use of extended relational alphabets in accuracy classifier systems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
XCS cannot learn all boolean functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Resource management and scalability of the XCSF learning classifier system
Theoretical Computer Science
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Engineering Applications of Artificial Intelligence
Selection strategy for XCS with adaptive action mapping
Proceedings of the 15th annual conference on Genetic and evolutionary computation
An examination of evolved behavior in two reinforcement learning systems
Decision Support Systems
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Despite two decades of work learning classifier systems researchers have had relatively little to say on the subject of what makes a problem diffcult for a classifier system. Wilson's accuracy-based XCS, a promising and increasingly popular classifier system, is, we feel, the natural first choice of classifier system with which to address this issue. To make the task more tractable we limit our considerations to a restricted, but very important, class of problems. Most significantly, we consider only single step reinforcement learning problems and the use of the standard binary/ternary classifier systems language. In addition to distinguishing several dimensions of problem complexity for XCS, we consider their interactions, identify bounding cases of diffculty, and consider complexity metrics for XCS. Based on these results we suggest a simple template for ternary single step test suites to more comprehensively evaluate classifier systems.