Machine Learning
Classifier Systems and the Animat Problem
Machine Learning
A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
DXCS: an XCS system for distributed data mining
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Improving XCS Performance by Distribution
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
CoXCS: A Coevolutionary Learning Classifier Based on Feature Space Partitioning
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Autonomous multi-processor-SoC optimization with distributed learning classifier systems XCS
Proceedings of the 8th ACM international conference on Autonomic computing
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This paper attempts to extend the XCS research by analyzing the impact of information exchange between XCS agents on classifier performance. Two types of information are exchanged and combined to improve classification performance. The first uncovers information contained in the signal patterns of collections of Homogeneous XCS classifiers. This information is used to determine which subsets of the state-space the XCS can be expected to be accurately classified. The second combines the results of XCS agents that are each tasked to solve different portions of the original problem. Results on the multiplexer (6, 11) indicate that given accurate problem domain assumptions, the Collective Behavior (CB-HXCS) method shows promise. Results show - at least in simulated multiplexer environments - that the HXCS is able to solve a well defined problem with less data than an individual XCS. This approach seems very promissing in real-world applications where data is incomplete, expensive or unreliable such as in financial or medical domains.