Real/binary-like coded versus binary coded genetic algorithms to automatically generate fuzzy knowledge bases: a comparative study-Reference-Cited by-同舟云学术

Real/binary-like coded versus binary coded genetic algorithms to automatically generate fuzzy knowledge bases: a comparative study

Published:2004-06 Issue:4 Volume:17 Page:313-325
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Achiche Sofiane,Baron Luc,Balazinski Marek

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference20 articles.

1. Tool wear monitoring using genetically-generated fuzzy knowledge bases;Achiche;Engineering Applications of Artificial Intelligence,2002

2. Application of fuzzy logic techniques to the selection of cutting parameters in machining processes;Balazinski;International Journal for Fuzzy Sets and Systems,1993

3. Balazinski, M., Achiche, S., Baron, L., 2000. Influence of optimization and selection criteria on genetically-generated fuzzy knowledge bases. International Conference on Advanced manufacturing Technology, (ICAMT2000) Johor Bahru, Malaysia, pp. 159–164.

4. Baron, L., 1998. Genetic algorithm for line extraction, Technical Report EPM/RT-98/06, École Polythechnique de Montréal, 19pp.

5. Fuzzy decisions system knowledge base generation using a genetic algorithm;Baron;International Journal of Approximate Reasoning,2002

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