Evolving simple feed-forward and recurrent ANNs for signal classification: A comparison

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Rivero:2009:IJCNN,

• author = "Daniel Rivero and Julian Dorado and Juan Rabufial and Alejandro Pazos",
• title = "Evolving simple feed-forward and recurrent ANNs for signal classification: A comparison",
• booktitle = "International Joint Conference on Neural Networks, IJCNN 2009",
• year = "2009",
• pages = "2685--2692",
• address = "Atlanta, Georgia, USA",
• month = jun # " 14-19",
• keywords = "genetic algorithms, genetic programming, evolutionary computation, feedforward neural nets, learning (artificial intelligence), recurrent neural nets, signal classification, EEG signals, classification tasks, epileptic seizures, evolutionary method, machine learning, parameter configurations, recurrent ANN, signal classification, simple feedforward ANN",
• DOI = "doi:10.1109/IJCNN.2009.5178621",
• abstract = "Among all of the Machine Learning techniques used for classification tasks, Artificial Neural Networks (ANNs) have obtained much success in their applications. However, their development usually requires a manual effort from the human expert in which several parameter configurations (architectures, training parameters, etc) are tried. This paper proposes a new evolutionary method that evolves ANNs without any participation from the human expert. This system can be used to evolve feed-forward and recurrent ANNs. A real-world problem has been used to test the behaviour of this system: detection of epileptic seizures in EEG signals. A comparison of the results obtained using recurrent and feedforward ANNs to solve this problem is presented in this paper. This comparison shows the good accuracies obtained by this method (almost 100percent). Moreover, these results show an important feature: the system tries to evolve simple ANNs, with a low number of neurons and connections (in many cases, the networks have only 1 hidden neuron).",
• notes = "also known as \cite{5178621}",

}

Genetic Programming entries for Daniel Rivero Cebrian Julian Dorado Juan Rabufial Alejandro Pazos Sierra

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