Bid competition and specificity reconsidered
Complex Systems
IEA/AIE '94 Proceedings of the 7th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
VCS: Variable Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Representational Difficulties with 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
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
General Schema Theory for Genetic Programming with Subtree-Swapping Crossover
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Two analysis tools to describe the operation of classifier systems
Two analysis tools to describe the operation of classifier systems
For real! XCS with continuous-valued inputs
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
Toward a better understanding of rule initialisation and deletion
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
| Hi-index | 0.01 |
It is proposed a way of increasing the cardinality of an alphabet used to write rules in a learning classifier system that extends the idea of relational schemata. Theoretical justifications regarding the possible reduction in the amount of rules for the solution of problems such extended alphabets (st -alphabets) imply are shown. It is shown that when expressed as bipolar neural networks, the matching process of rules over st -alphabets strongly resembles a gene expression mechanism applied to a system over {0,1,#}. In spite of the apparent drawbacks the explicit use of such relational alphabets would imply, their successful implementation in an information gain based classifier system (IGCS) is presented.