F. Marin and F. Sandoval, "Genetic Synthesis of Discrete-Time Recurrent Neural Network," Lecture Notes in Computer Science, vol. 686, pp. 179--184, 1993. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Pareto Evolutionary Neural Networks - Fieldsend, Singh (2003) (Correct)
.... to use of those evolutionary computation (EC) methods which have previously been applied to uni objective NN design, genetic algorithms (GAs) evolution strategies (ES) and particle swarm optimisation (PSO) GAs have previously be used for feature selection [8, 53] and topography selection [2, 5, 29, 35, 36, 38, 52] and ESs have been used for weight optimisation [21, 42, 45, 55] and adaptive topography selection [15, 37, 57] The recent EC technique of PSO [27] has also proved popular as a uni objective NN optimiser [10, 12, 13, 26, 48] 2 Multi objective evolutionary neural network flamework The use of ....
F.J. Marin and F. Sandoval. Genetic Synthesis of Discrete-Time Recurrent Neural Network. Lecture Notes in Computer Science, 686:179-184, 1993.
Efficient Evolution of Asymmetric Recurrent Neural Networks.. - Pujol, Poli (1997) (1 citation) (Correct)
.... on the neural network directly, and rely exclusively on mutation [10, 11, 12, 13] or combine mutation with training [14] Methods based on genetic algorithms usually represent the structure and the weights of ANNs as a string of 1 bits or as a combination of bits, integers and real numbers [15, 16, 17, 18, 19, 20], and perform the crossover operation as if the network were a linear structure. However, neural networks cannot naturally be represented as vectors. They are oriented graphs, whose nodes are neurons and whose arcs are synaptic connections. Therefore, it is arguable that any efficient approach to ....
F. Marin and F. Sandoval. Genetic synthesis of discrete-time recurrent neural network. In Proceedings of International Workshop on Artificial Neural Network (IWANN), pages 179--184. Springer-Verlag, 1993.
Efficient Evolution of Asymmetric Recurrent Neural Networks.. - Pujol, Poli (1998) (1 citation) (Correct)
.... operate on the neural network directly, and rely exclusively on mutation [10, 11, 12, 13] or combine mutation with training [14] Methods based on genetic algorithms usually represent the structure and the weights of ANNs as a string of bits or as a combination of bits, integers and real numbers [15, 16, 17, 18, 19, 20], and perform the crossover operation as if the network were a linear structure. However, neural networks cannot naturally be represented as vectors. They are oriented graphs, whose nodes are neurons and whose arcs are synaptic connections. Therefore, it is arguable that any efficient approach to ....
F. Marin and F. Sandoval. Genetic synthesis of discrete-time recurrent neural network. In Proceedings of International Workshop on Artificial Neural Network (IWANN), pages 179--
Pareto Evolutionary Neural Networks - Jonathan Fieldsend Member (Correct)
No context found.
F. Marin and F. Sandoval, "Genetic Synthesis of Discrete-Time Recurrent Neural Network," Lecture Notes in Computer Science, vol. 686, pp. 179--184, 1993.
Evolutionary Design of Neural Architectures - A.. - Balakrishnan, Honavar (1995) (27 citations) (Correct)
No context found.
F.J. Marin and F. Sandoval. Genetic Synthesis of Discrete-Time Recurrent Neural Network. New Trends in Neural Computation, Springer-Verlag, pages 179--184, 1993.
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