The C++ standard library: a tutorial and reference
The C++ standard library: a tutorial and reference
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
For real! XCS with continuous-valued inputs
Evolutionary Computation
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
GECCO '06 Genetic and Evolutionary Computation Conference
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
Fast rule matching for learning classifier systems via vector instructions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
A mixed discrete-continuous attribute list representation for large scale classification domains
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A population-based approach to finding the matchset of a learning classifier system efficiently
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Speedup character-based matching in learning classifier systems with Xor
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Fast prediction computation in learning classifier systems using CUDA
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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We investigate rule matching in learning classifier systems for problems involving binary and real inputs. We consider three rule encodings: the widely used character-based encoding, a specificity-based encoding, and a binary encoding used in Alecsys. We compare the performance of the three algorithms both on matching alone and on typical test problems. The results on matching alone show that the population generality influences the performance of the matching algorithms based on string representations in different ways. Character-based encoding becomes slower and slower as generality increases, specificity-based encoding becomes faster and faster as generality increases. The results on typical test problems show that the specificity-based representation can halve the time required for matching but also that binary encoding is about ten times faster on the most difficult problems. Moreover, we extend specificity-based encoding to real-inputs and propose an algorithm that can halve the time require for matching real inputs using an interval-based representation.