We discuss the problem of learning fuzzy rules using Evolutionary Learning techniques, such as Genetic Algorithms and Learning Classifier Systems. We present ELF, a system able to evolve a population of fuzzy rules to obtain a sub-optimal Fuzzy Logic Controller. ELF tackles some of the problems typical of the Evolutionary Learning approach: competition and cooperation between fuzzy rules, evolution of general fuzzy rules, imperfect reinforcement programs, fast evolution for real-time applications, dynamic evolution of the focus of the search. We also present some of the results obtained from the application of ELF to the development of Fuzzy Logic Controllers for autonomous agents and for the classical cart-pole problem.

Evolutionary Learning of Fuzzy rules: competition and cooperation

BONARINI, ANDREA
1996

Abstract

We discuss the problem of learning fuzzy rules using Evolutionary Learning techniques, such as Genetic Algorithms and Learning Classifier Systems. We present ELF, a system able to evolve a population of fuzzy rules to obtain a sub-optimal Fuzzy Logic Controller. ELF tackles some of the problems typical of the Evolutionary Learning approach: competition and cooperation between fuzzy rules, evolution of general fuzzy rules, imperfect reinforcement programs, fast evolution for real-time applications, dynamic evolution of the focus of the search. We also present some of the results obtained from the application of ELF to the development of Fuzzy Logic Controllers for autonomous agents and for the classical cart-pole problem.
Fuzzy Modelling: Paradigms and Practice
0792397037
Fuzzy systems; machine learning; reinforcement learning; learning fuzzy logic classifiers
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/666819
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