Partially-Connected Artificial Neural Networks Developed by Grammatical Evolution for Pattern Recognition Problems

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@InCollection{series/sci/Quiroz-RamirezEODSSCEO18,
  author =       "Olga Quiroz-Ramirez and Andres Espinal and 
                 Manuel Ornelas-Rodriguez and Alfonso Rojas Dominguez and 
                 Daniela Sanchez and Hector Jose Puga Soberanes and 
                 Martin Carpio and Luis Ernesto Mancilla Espinoza and 
                 Janet Ortiz-Lopez",
  title =        "Partially-Connected Artificial Neural Networks
                 Developed by Grammatical Evolution for Pattern
                 Recognition Problems",
  booktitle =    "Fuzzy Logic Augmentation of Neural and Optimization
                 Algorithms: Theoretical Aspects and Real Applications",
  publisher =    "Springer",
  year =         "2018",
  editor =       "Oscar Castillo and Patricia Melin and 
                 Janusz Kacprzyk",
  volume =       "749",
  series =       "Studies in Computational Intelligence",
  pages =        "99--112",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  bibdate =      "2018-01-16",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/series/sci/sci749.html#Quiroz-RamirezEODSSCEO1",
  isbn13 =       "978-3-319-71007-5",
  DOI =          "doi:10.1007/978-3-319-71008-2_9",
  abstract =     "Evolutionary Artificial Neural Networks (EANNs) are a
                 special case of Artificial Neural Networks (ANNs) for
                 which Evolutionary Algorithms (EAs) are used to modify
                 or create them. EANNs adapt their defining components
                 ad hoc for solving a particular problem with little or
                 no intervention of human expert. Grammatical Evolution
                 (GE) is an EA that has been used to indirectly develop
                 ANNs, among other design problems. This is achieved by
                 means of three elements: a Context-Free Grammar (CFG)
                 which includes the ANNs defining components, a search
                 engine that drives the search process and a mapping
                 process. The last component is a heuristic for
                 transforming each GE's individual from its genotypic
                 form into its phenotypic form (a functional ANN).
                 Several heuristics have been proposed as mapping
                 processes in the literature; each of them may transform
                 a specific individual's genotypic form into a very
                 different phenotypic form. In this paper,
                 partially-connected ANNs are automatically developed by
                 means of GE. A CFG is proposed to define the
                 topologies, a Genetic Algorithm (GA) is the search
                 engine and three mapping processes are tested for this
                 task; six well-known pattern recognition benchmarks are
                 used to statistically compare them. The aim of this
                 work for using and comparing different mapping process
                 is to analyse them for setting the basis of a generic
                 framework to automatically create ANNs.",
}

Genetic Programming entries for Olga Quiroz-Ramirez Andres Espinal Jimenez Manuel Ornelas-Rodriguez Alfonso Rojas Dominguez Daniela Sanchez Hector J Puga Juan Martin Carpio Luis Ernesto Mancilla Espinoza Janet Ortiz-Lopez

Citations