Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Adding temporary memory to ZCS
Adaptive Behavior
Learning to solve multiple goals
Learning to solve multiple goals
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Consideration of Multiple Objectives in Neural Learning Classifier Systems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
What Is a Learning Classifier System?
Learning Classifier Systems, From Foundations to Applications
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
What Makes a Problem Hard for XCS?
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS
Learning Classifier Systems
XCS cannot learn all boolean functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Random artificial incorporation of noise in a learning classifier system environment
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Two-cornered learning classifier systems for pattern generation and classification
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Enhancing learning capabilities by XCS with best action mapping
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Engineering Applications of Artificial Intelligence
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Wilson's XCS is a clear departure from earlier classifier systems in terms of the way it calculates the fitness of classifiers for use in the genetic algorithm. Despite the growing body of work on XCS and the advantages claimed for it there has been no detailed comparison of XCS and traditional strength-based systems. This work takes a step towards rectifying this situation by surveying a number of issues related to the change in fitness. I distinguish different definitions of overgenerality for strength and accuracy-based fitness and analyse some implications of the use of accuracy, including an apparent advantage in addressing the explore/exploit problem. I analyse the formation of strong overgenerals, a major problem for strength-based systems, and illustrate their dependence on biased reward functions. I consider motivations for biasing reward functions in single step environments, and show that non-trivial multi step environments have biased Q-functions. I conclude that XCS's accuracy-based fitness appears to have a number of significant advantages over traditional strength-based fitness.