Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Initial Modifications to XCS for Use in Interactive Evolutionary Design
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Be real! XCS with continuous-valued inputs
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Initial results from the use of learning classifier systems to control in vitro neuronal networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
New approach for extracting knowledge from the XCS learning classifier system
International Journal of Hybrid Intelligent Systems
An analysis of matching in learning classifier systems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Coevolutionary bid-based genetic programming for problem decomposition in classification
Genetic Programming and Evolvable Machines
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Organic Control of Traffic Lights
ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing
QFCS: A Fuzzy LCS in Continuous Multi-step Environments with Continuous Vector Actions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
A Learning Classifier System Approach to Relational Reinforcement Learning
Learning Classifier Systems
Intrusion detection with evolutionary learning classifier systems
Natural Computing: an international journal
Observer-invariant histopathology using genetics-based machine learning
Natural Computing: an international journal
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
New entropy model for extraction of structural information from XCS population
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Real-Valued GCS Classifier System
International Journal of Applied Mathematics and Computer Science
Towards learning classifier systems for continuous-valued online environments
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Analyzing parameter sensitivity and classifier representations for real-valued XCS
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
The class imbalance problem in UCS classifier system: a preliminary study
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Using XCS to describe continuous-valued problem spaces
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
A first assessment of the use of extended relational alphabets in accuracy classifier systems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
On the problems of using learning classifier systems for fraud detection
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
To handle real valued input in XCS: using fuzzy hyper-trapezoidal membership in classifier condition
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Towards final rule set reduction in XCS: a fuzzy representation approach
Proceedings of the 13th annual conference on Genetic and evolutionary computation
PCA for improving the performance of XCSR in classification of high-dimensional problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Towards a mapping of modern AIS and LCS
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Resource management and scalability of the XCSF learning classifier system
Theoretical Computer Science
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Many real-world problems are not conveniently expressed using the ternary representation typically used by Learning Classifier Systems and for such problems an interval-based representation is preferable. We analyse two interval-based representations recently proposed for XCS, together with their associated operators and find evidence of considerable representational and operator bias. We propose a new interval-based representation that is more straightforward than the previous ones and analyse its bias. The representations presented and their analysis are also applicable to other Learning Classifier System architectures.We discuss limitations of the real multiplexer problem, a benchmark problem used for Learning Classifier Systems that have a continuous-valued representation, and propose a new test problem, the checkerboard problem, that matches many classes of real-world problem more closely than the real multiplexer.Representations and operators are compared using both the real multiplexer and checkerboard problems and we find that representational, operator and sampling bias all affect the performance of XCS in continuous-valued environments.