Title :
Crossover enhancements in GEFREX
Author_Institution :
Dept. of Phys. & Astron., Catania Univ., Italy
fDate :
7/1/2005 12:00:00 AM
Abstract :
This letter describes some improvements to the crossover operator in the genetic-neuro-fuzzy algorithm called genetic fuzzy rule extractor (GEFREX). Although, the new crossovers studied are very simple, the performance of GEFREX, in terms of learning time, is decidedly improved.
Keywords :
fuzzy logic; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); GEFREX; crossover enhancement; genetic fuzzy rule extractor; genetic neuro fuzzy algorithm; Acceleration; Fuzzy logic; Fuzzy sets; Genetic algorithms; Least squares approximation; Machine learning; Machine learning algorithms; Neural networks; Phase detection; Supervised learning; Crossover; fuzzy logic; genetic algorithms (GAs); machine learning; neural networks; Algorithms; Computer Simulation; Fuzzy Logic; Models, Statistical; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.849841
