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Connectionist Learning Classifier System

By Alexey Vasilyev


Impetuous development of artificial neural networks makes it possible to transfer many ideas from this area into adjacent areas. This work investigates an opportunity of mapping learning classifier systems (LCS) into artificial neural networks (ANN). Possible learning types for hybrid connectionist classifier system (CLCS) for multi-step problems are derived. Transformation's opportunity of the existing LCS into CLCS is shown. Heuristic LCS and CLCS comparisons in the dynamic environment show superiority of CLCS over LCS

Topics: artificial neural networks, learning classifier systems, reinforcement learning
Year: 2002
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