Neural network construction and training using grammatical evolution

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@Article{Tsoulos2008269,

• author = "Ioannis Tsoulos and Dimitris Gavrilis and Euripidis Glavas",
• title = "Neural network construction and training using grammatical evolution",
• journal = "Neurocomputing",
• volume = "72",
• number = "1-3",
• pages = "269--277",
• year = "2008",
• note = "Machine Learning for Signal Processing (MLSP 2006) / Life System Modelling, Simulation, and Bio-inspired Computing (LSMS 2007)",
• ISSN = "0925-2312",
• DOI = "doi:10.1016/j.neucom.2008.01.017",
• URL = "http://www.sciencedirect.com/science/article/B6V10-4S1C894-3/2/9beaf5f426239399e63b31456dcbc52a",
• keywords = "genetic algorithms, genetic programming, Grammatical evolution, Neural network, Context-free grammar",
• abstract = "The term neural network evolution usually refers to network topology evolution leaving the network's parameters to be trained using conventional algorithms. In this paper we present a new method for neural network evolution that evolves the network topology along with the network parameters. The proposed method uses grammatical evolution to encode both the network and the parameters space. This allows for a better description of the network using a formal grammar allowing the network architect to shape the resulting search space in order to meet each problem requirement. The proposed method is compared with other three methods for neural network training and is evaluated using 9 known classification problems and 9 known regression problems. In all 18 datasets, the proposed method outperforms its competitors.",

}

Genetic Programming entries for Ioannis G Tsoulos Dimitris Gavrilis Euripidis Glavas

Citations