Introduction to the theory of neural computation
Introduction to the theory of neural computation
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Genetic local search in combinatorial optimization
CO89 Selected papers of the conference on Combinatorial Optimization
A simple heuristic based genetic algorithm for the maximum clique problem
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
An adaptive evolutionary algorithm for the satisfiability problem
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
A GA-based fuzzy adaptive learning control network
Fuzzy Sets and Systems
An Introduction to Fuzzy Logic Applications
An Introduction to Fuzzy Logic Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
Evolutionary algorithms for the satisfiability problem
Evolutionary Computation
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
A New Approach for Extracting Rules from a Trained Neural Network
EPIA '97 Proceedings of the 8th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neurofuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach.