Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
VCS: Variable Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Relational Schemata: A Way to Improve the Expressiveness of Classifiers
Proceedings of the 6th International Conference on Genetic Algorithms
An Introduction to Learning Fuzzy Classifier Systems
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
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
An Algorithmic Description of XCS
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
What Makes a Problem Hard for XCS?
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
For real! XCS with continuous-valued inputs
Evolutionary Computation
A first order logic classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Policy transfer with a relational learning classifier system
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Using convex hulls to represent classifier conditions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fuzzy-UCS: preliminary results
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
An approach to analyze the evolution of symbolic conditions in learning classifier systems
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Investigating scaling of an abstracted LCS utilising ternary and s-expression alphabets
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Discrete dynamical genetic programming in XCS
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
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It is proposed to extend the typical ternary {0,1,#} representation used in classifier systems by including source and target relational characters, with mappings linking targets to sources expressed as rules of another system over ternary alphabet. After transforming such a system into bipolar neural networks, it is shown that the influence of these mappings can be interpreted as the creation of holes in the hyperplanes of rules over ternary alphabet. Relational schemata are reviewed as the main antecedent of extended alphabets (st-alphabets) presented, and it is shown that st-alphabets inherit from them the features that led their creators to refuse implementing them explicitly. Successful experiments on the parity problem and Woods2 are presented after showing two approaches for the measurement of the expressive power of st-alphabets, the minimal modifications that can be performed in an accuracy classifier system (XCS) to experiment with populations of rules over them, and how the use of these alphabets impact the rule evolution pressures identified in a run of a XCS. The article ends with suggestions for future exploitations of the features of st-alphabets in modular and hierarchical problems.