Classifier systems and genetic algorithms
Artificial Intelligence
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Artificial Intelligence Review - Special issue on lazy learning
How neutral networks influence evolvability
Complexity
Classifiers that approximate functions
Natural Computing: an international journal
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Extending XCSF beyond linear approximation
GECCO '05 Proceedings of the 7th annual conference on Genetic and 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
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
An analysis of matching in learning classifier systems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Context-dependent predictions and cognitive arm control with XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Self-adaptive mutation in XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Real-Valued LCS Using UNDX for Technology Extraction
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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
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
Modularization of xcsf for multiple output dimensions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Filtering sensory information with XCSF: improving learning robustness and control performance
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The learning classifier system XCS is an iterative rule-learning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classification and reinforcement learning tasks, XCS was applied as an effective function approximator. Hereby, XCS learns space partitions to enable a maximally accurate and general function approximation. Recently, the function approximation approach was improved by replacing (1) hyperrectangular conditions with hyper-ellipsoids and (2) iterative linear approximation with the recursive least squares method. This paper combines the two approaches assessing the usefulness of each. The evolutionary process is further improved by changing the mutation operator implementing an angular mutation that rotates ellipsoidal structures explicitly. Both enhancements improve XCS performance in various non-linear functions. We also analyze the evolving ellipsoidal structures confirming that XCS stretches and rotates the evolving ellipsoids according to the shape of the underlying function. The results confirm that improvements in both the evolutionary approach and the gradient approach can result in significantly better performance.