Adaptive signal processing
Bucket brigade performance: I. Long sequences of classifiers
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Machine learning applications to job shop scheduling
IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Classifier Systems and the Animat Problem
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
A neural model of adaptive behavior
A neural model of adaptive behavior
Empirical studies of default hierarchies and sequences of rules in learning classifier systems
Empirical studies of default hierarchies and sequences of rules in learning classifier systems
A Learning Classifier Systems Bibliography
Learning Classifier Systems, From Foundations to Applications
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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Classifier systems are production rule systems that automatically generate populations of rules cooperating to accomplish desired tasks. The genetic algorithm is the systems' discovery mechanism, and its effectiveness is dependent in part on the accurate estimation of the relative merit of each of the rules (classifiers) in the current population. Merit is estimated conventionally by use of the bucket brigade for credit assignment. This paper addresses the adequacy of the bucket brigade and provides a preliminary exploration of two variants in conjunction with enumerated rules and with discovery. In limited experiments, a variant that combines the bucket brigade, "classifier chunking," and "backwards averaging" has yielded improved performance on simple maze problems. Tentative similarities between this hybrid and Sutton's Adaptive Heuristic Critic (AHC) are suggested.