Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neuro-Dynamic Programming
Classifiers that approximate functions
Natural Computing: an international journal
Investigating Generalization in the Anticipatory Classifier System
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Evolving Behaviors for Cooperating Agents
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Short communication: Mining knowledge from data using Anticipatory Classifier System
Knowledge-Based Systems
ZCS Revisited: Zeroth-Level Classifier Systems for Data Mining
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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The emergence of eXtended Classifier Systems (XCS) raised the bar for Learning Classifier Systems by incorporating the accuracies of the rules in the LCS's traditional reinforcement mechanism. However, neither XCS nor its extensions take into account the nature of a classifier's experience of attending the action set. We introduce an experience-evaluation mechanism that, once added to the traditional XCS, would assigns to each member of the action set a success rate indicating how effectively the classifier has contributed to the correct responding of the system to the environment's queries. Application of the augmented system (called SRXCS) to several benchmark problems shows that the proposed mechanism enhances XCS' classification capability and its rate of convergence at the same time. Application results indicate that SRXCS performs notably better on both pattern association and pattern recognition tasks. The applicability and efficiency of the proposed mechanism is further demonstrated through solving a fairly complex path planning problem for an autonomous mobile robot in a dynamic environment.