Neurocomputing: foundations of research
Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Reinforcement Learning
Classifiers that approximate functions
Natural Computing: an international journal
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Micro-Genetic Algorithm for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Extending XCSF beyond linear approximation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An anticipatory approach to improve XCSF
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
The micro genetic algorithm 2: towards online adaptation in evolutionary multiobjective optimization
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
An evolutionary function approximation approach to compute prediction in XCSF
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Learning Classifier Systems are Evolutionary Learning mechanisms which combine Genetic Algorithm and the Reinforcement Learning paradigm. Learning Classifier Systems try to evolve state-action-reward mappings to propose the best action for each environmental state to maximize the achieved reward. In the first versions of learning classifier systems, state-action pairs can only be mapped to a constant real-valued reward. So to model a fairly complex environment, LCSs had to develop redundant state-action pairs which had to be mapped to different reward values. But an extension to a well-known LCS, called Accuracy Based Learning Classifier System or XCS, was recently developed which was able to map state-action pairs to a linear reward function. This new extension, called XCSF, can develop a more compact population than the original XCS. But some further researches have shown that this new extension is not able to develop proper mappings when the input parameters are from certain intervals. As a solution to this issue, in our previous works, we proposed a novel solution inspired by the idea of using evolutionary approach to approximate the reward landscape. The first results seem promising, but our approach, called XCSFG, converged to the goal very slowly. In this paper, we propose a new extension to XCSFG which employs micro-GA which its needed population is extremely smaller than simple GA. So we expect micro-GA to help XCSFG to converge faster. Reported results show that this new extension can be assumed as an alternative approach in XCSF family with respect to its convergence speed, approximation accuracy and population compactness.