Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Classifiers that approximate functions
Natural Computing: an international journal
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
What Makes a Problem Hard for XCS?
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
For real! XCS with continuous-valued inputs
Evolutionary Computation
Cognitive systems based on adaptive algorithms
ACM SIGART Bulletin
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Incremental Online Learning in High Dimensions
Neural Computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension
Evolutionary Computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Context-dependent predictions and cognitive arm control with XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning sensorimotor control structures with XCSF: redundancy exploitation and dynamic control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A comparative study: function approximation with LWPR and XCSF
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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
XCSF with local deletion: preventing detrimental forgetting
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Learning local linear Jacobians for flexible and adaptive robot arm control
Genetic Programming and Evolvable Machines
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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It has been shown many times that the evolutionary online learning XCS classifier system is a robustly generalizing reinforcement learning system, which also yields highly competitive results in data mining applications. The XCSF version of the system is a real-valued function approximation system, which learns piecewise overlapping local linear models to approximate an iteratively sampled function. While the theory on the binary domain side goes as far as showing that XCS can PAC learn a slightly restricted set of k-DNF problems, theory for XCSF is still rather sparse. This paper takes the theory from the XCS side and projects it onto the real-valued XCSF domain. For a set of functions, in which fitness guidance is given, we even show that XCSF scales optimally with respect to the population size, requiring only a constant overhead to ensure that the evolutionary process can locally optimize the evolving structures. Thus, we provide foundations concerning scalability and resource management for XCSF. Furthermore, we reveal dimensions of problem difficulty for XCSF - and local linear learners in general - showing how structural alignment, that is, alignment of XCSF's solution representation to the problem structure, can reduce the complexity of challenging problems by orders of magnitude.