Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Fundamentals of Computer Organization and Design
Fundamentals of Computer Organization and Design
Classifiers that approximate functions
Natural Computing: an international journal
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
Classifier systems that compute action mappings
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
XCS cannot learn all boolean functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Automatically defined functions for learning classifier systems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Extracting and using building blocks of knowledge in learning classifier systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
XCSR with computed continuous action
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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XCS is a learning classifier system that uses accuracy-based fitness to learn a problem. Commonly, a classifier rule in XCS is encoded using a ternary alphabet based condition and a numeric action. Previously, we implemented a code-fragment action based XCS, called XCSCFA, where the typically used numeric action was replaced by a genetic programming like tree-expression. In XCSCFA, the action value in a classifier was computed by loading the terminal symbols in the action-tree with the corresponding binary values in the condition of the classifier rule. This enabled accurate, general and compact rule sets to be simply produced. The main contribution of this work is to investigate an intuitive way, i.e. using the environmental instance, to compute the action value in XCSCFA, instead of the condition of the classifier rule. The methods will be compared in five different Boolean problem domains, i.e. multiplexer, even-parity, majority-on, design verification, and carry problems. The environmental instance based XCSCFA approach had better classification performance than standard XCS as well as classifier condition based XCSCFA and solved all the problems experimented here. In addition it produced more general and compact classifier rules in the final solution. However, classifier condition based XCSCFA has the advantage of producing the optimal classifiers such that they are clearly separated from the sub-optimal ones in certain domains.