Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Implicit niching in a learning classifier system: Nature's way
Evolutionary Computation
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
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Michigan-style Classifier Systems use Genetic Algorithms to facilitate rule-discovery, where rule fitness has traditionally been prediction-based. Current research has shifted to the use of accuracy-based fitness. This paper presents a simple Markov model of the algorithm in such systems, allowing comparison between the two forms of rule utility measure. Using a single-step task the previously discussed benefits of accuracy over prediction are clearly shown with regard to overgeneral rules. The effects of a niche-based algorithm (maximal generality) are also briefly examined, as are the effects of mutation under the two fitness schemes.