Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
For real! XCS with continuous-valued inputs
Evolutionary Computation
Use of learning classifier system for inferring natural language grammar
Design and application of hybrid intelligent systems
How to use crowding selection in Grammar-based Classifier System
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Classifier fitness based on accuracy
Evolutionary Computation
Playing a toy-grammar with GCS
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Towards 3D modeling of interacting TM helix pairs based on classification of helix pair sequence
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Topographic map object classification using real-value grammar classifier system
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
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Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.