Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The compact classifier system: motivation, analysis, and first results
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Knowledge-based fast evaluation for evolutionary learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
Do not match, inherit: fitness surrogates for genetics-based machine learning techniques
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An analysis of matching in learning classifier systems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Observer-invariant histopathology using genetics-based machine learning
Natural Computing: an international journal
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
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
Self-adaptation of learning rate in XCS working in noisy and dynamic environments
Computers in Human Behavior
Self-adaptation of parameters in a learning classifier system ensemble machine
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Over the last ten years XCS has become the standard for Michigan-style learning classifier systems (LCS). Since the initial CS-1 work conceived by Holland, classifiers (rules) have widely used a ternary condition alphabet {0,1,#} for binary input problems. Most of the freely available implementations of this ternary alphabet in XCS rely on character-based encodings---easy to implement, not memory efficient, and expensive to compute. Profiling of freely available XCS implementations shows that most of their execution time is spent determining whether a rule is match or not, posing a serious threat to XCS scalability. In the last decade, multimedia and scientific applications have pushed CPU manufactures to include native support for vector instruction sets. This paper presents how to implement efficient condition encoding and fast rule matching strategies using vector instructions. The paper elaborates on Altivec (PowerPC G4, G5) and SSE2 (Intel P4/Xeon and AMD Opteron) instruction sets producing speedups of XCS matching process beyond ninety times. Moreover, such a vectorized matching code will allow to easily scale beyond tens of thousands of conditions in a reasonable time. The proposed fast matching scheme also fits in any other LCS other than XCS.