Classifier systems and genetic algorithms
Artificial Intelligence
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Anytime learning and adaptation of structured fuzzy behaviors
Adaptive Behavior - Special issue on environment structure and behavior
Fuzzy evolutionary computation
Fuzzy evolutionary computation
Classifier Systems and the Animat Problem
Machine Learning
A New Approach to Fuzzy Classifier Systems
Proceedings of the 5th International Conference on Genetic Algorithms
The Fuzzy Classifier System: Motivations and first Results
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An Introduction to Learning Fuzzy Classifier Systems
Learning Classifier Systems, From Foundations to Applications
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
An approach to the design of reinforcement functions in real world,agent-based applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present first results from a comparison between a Fuzzy Classifier System operating at the level of whole rule-bases, and three variants of one that operates at the level of individual rules. The application domain is mobile robotics, and the problem is autonomous acquisition of an "investigative" obstacle avoidance competency. The Fuzzy Classifier Systems operate on the rules of fuzzy controllers with pre-defined fuzzy membership functions. Generally, all of the methods used were capable of producing fuzzy controllers with competencies that exceeded that of a simple hand-coded fuzzy controller that we had devised. The approach operating at the level of whole rule-bases yielded more robust and stable convergence on high performance solutions than any other architecture presented here. It is clear from the results that more work needs to be done to unravel the disappointing convergence dynamics of the algorithms operating at the level of individual rules.