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Abstract: . Recurrent neural networks are particularly useful for processing time sequences and simulating dynamical systems. However, methods for building recurrent architectures have been hindered by the fact that available training algorithms are considerably more complex than those for feedforward networks. In this paper, we present a new method to build recurrent neural networks based on evolutionary computation, which combines a linear chromosome with a twodimensional representation inspired... (Update)
Context of citations to this paper: More
.... algorithm (GA) to evolve both neural networks and finite automata capable of following the John Muir trail, Angeline93] and [Pujol98] use, respectively, a GA and Parallel Distributed Genetic Programming (PDGP) to evolve neural networks capable of following the same...
...amount of work has been done on the evolution of the weights and or the topology of neural networks. See for example [20, 35, 36, 37, 54]. However only a relatively small amount of previous work has been reported on the evolution of learning rules for neural networks....
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BibTeX entry: (Update)
J. Pujol and R.Poli, "Efficient Evolution of Asymmetric Recurrent Neural Networks Using a PDGP-inspired Two-dimensional Representation ", Proceedings of EuroGP'98, First European Workshop on Genetic Programming, April 1998, Springer. http://citeseer.ist.psu.edu/article/pujol98efficient.html More
@article{ pujol98efficient,
author = "Jo{\~a}o Carlos Figueira Pujol and Riccardo Poli",
title = "Efficient Evolution of Asymmetric Recurrent Neural Networks Using a {PDGP}-inspired Two-Dimensional Representation",
journal = "Lecture Notes in Computer Science",
volume = "1391",
pages = "130--??",
year = "1998",
url = "citeseer.ist.psu.edu/article/pujol98efficient.html" }
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