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
Evolving artificial intelligence
Evolving artificial intelligence
Self-Adaptive Mutation in ZCS Controllers
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Improving the Performance of Genetic Algorithms in Classifier Systems
Proceedings of the 1st International Conference on Genetic Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Zcs: A zeroth level classifier system
Evolutionary Computation
Implicit niching in a learning classifier system: Nature's way
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness
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
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems
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Michigan-style Classifier Systems use Genetic Algorithms to facilitate rule-discovery. This paper presents a simple Markov model of the algorithm in such systems, with the aim of examining the effects of different types of interdependence between niches in multi-step tasks. Using the model it is shown that the existence of, what is here termed, partner rule variance can have significant and detrimental effects on the Genetic Algorithm's expected behaviour. Suggestions are made as to how to reduce these effects, making connections with other recent work in the area.