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Classifier Fitness Based on Accuracy
A classifier system, XCS, is investigated, in which each classifier maintains a prediction of expected payoff, but the classifier's fitness is given by a measure of the prediction's accuracy, making it suitable for a wide range of reinforcement learning situations where generalization over states is desirable.
ZCS: A Zeroth Level Classifier System
A basic classifier system, ZCS, is presented that keeps much of Holland's original framework but simplifies it to increase understandability and performance. ZCS's relation to Q-learning is brought
An algorithmic description of XCS
Abstract A concise description of the XCS classifier system's parameters, structures, and algorithms is presented as an aid to research. The algorithms are written in modularly structured pseudo code
Get Real! XCS with Continuous-Valued Inputs
A modified XCS classifier system is described that learns a non-linear real-vector classification task.
Generalization in the XCS Classifier System
The amount of computation per input is considerably larger for XCS, perhaps by enough to make the two approaches approximately equal in computational effort, but this will begin to offset XCS's enormous advantage in learning rate per input only if new inputs are available faster than XCS can process them.
Classifiers that approximate functions
A classifier system, XCSF, is introduced in which the predictionestimation mechanism is used to learn approximations to functions.The addition of weight vectors to the classifiers
Toward the Evolution of Dynamical Neural Networks for Minimally Cognitive Behavior
The results of preliminary experiments on the evolution of dynamical neural networks for visually-guided orientation, object discrimination and accurate pointing with a simple manipulator to objects appearing in its field of view are presented.
Mining Oblique Data with XCS
The classifier system XCS was investigated for data mining applications where the dataset discrimination surface (DS) is generally oblique to the attribute axes and shows promise from both performance and pattern discovery viewpoints.
Toward a theory of generalization and learning in XCS
This work starts from Wilson's generalization hypothesis, which states that XCS has an intrinsic tendency to evolve accurate, maximally general classifiers, and derives a simple equation that supports the hypothesis theoretically.
Knowledge Growth in an Artificial Animal
This paper describes work using an artificial, behaving, animal model (termed an “ani-mat”) to study intelligence at a primitive level, and wishes to provide the animat with adaptive mechanisms which yield rapid and solid improvement but themselves contain minimal a priori information.