title: A real-coded genetic algorithm for training recurrent neural networks.
abstract: The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference.
authors.affiliation: Department of Computer Science and Artificial Intelligence, ETSI Informática, University of Granada, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Blanco
authors.forename: M
authors.initials: M
authors.lastname: Delgado
authors.forename: M C
authors.initials: MC
authors.lastname: Pegalajar
mesh_terms: D000465:Algorithms; D017143:Fuzzy Logic; D005815:Genetic Code; D008962:Models, Theoretical; D016571:Neural Networks, Computer