@Article{CarvalhoRamoChav:2011:MeFeAr,
               author = "Carvalho, A. R and Ramos, F. M. and Chaves, A. A.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Metaheuristics for the feedforward artificial neural network (ANN) 
                         architecture optimization problem",
              journal = "Neural Computing and Applications",
                 year = "2011",
               volume = "20",
               number = "8",
                pages = "1273 - 1284",
                month = "Dec.",
             abstract = "This article deals with evolutionary artificial neural network 
                         (ANN) and aims to propose a systematic and automated way to find 
                         out a proper network architecture. To this, we adapt four 
                         metaheuristics to resolve the problem posed by the pursuit of 
                         optimum feedforward ANN architecture and introduced a new criteria 
                         to measure the ANN performance based on combination of training 
                         and generalization error. Also, it is proposed a new method for 
                         estimating the computational complexity of the ANN architecture 
                         based on the number of neurons and epochs needed to train the 
                         network. We implemented this approach in software and tested it 
                         for the problem of identification and estimation of pollution 
                         sources and for three separate benchmark data sets from UCI 
                         repository. The results show the proposed computational approach 
                         gives better performance than a human specialist, while offering 
                         many advantages over similar approaches found in the literature. © 
                         2010 Springer-Verlag London Limited.",
                  doi = "10.1007/s00521-010-0504-3",
                  url = "http://dx.doi.org/10.1007/s00521-010-0504-3",
                 issn = "0941-0643",
             language = "en",
           targetfile = "carvalho.pdf",
        urlaccessdate = "21 maio 2024"
}