Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Concerning the emergence of tag-mediated lookahead in classifier systems
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Triggered rule discovery in classifier systems
Proceedings of the third international conference on Genetic algorithms
Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Evolutionary Computation
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
Consideration of Multiple Objectives in Neural Learning Classifier Systems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Lookahead And Latent Learning In ZCS
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Self-Adaptive Classifier System
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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
An Algorithmic Description of XCS
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
YACS: Combining Dynamic Programming with Generalization in Classifier Systems
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
An Algorithmic Description of ACS2
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Self-adaptive mutation in XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of an accuracy-based scheme where fitness is based on a rule's ability to predict the expected payoff from its use. Learning Classifier Systems that build anticipations of the expected states following their actions are also a focus of current research. This paper presents a simple but effective learning classifier system of this last type, using payoff-based fitness, with the aim of enabling the exploration of their basic principles, i.e., in isolation from the many other mechanisms they usually contain. The system is described and modelled, before being implemented. Comparisons to an equivalent accuracy-based system show similar performance. The use of self-adaptive mutation in such systems in general is then considered.