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

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Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 749))

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 analyze them for setting the basis of a generic framework to automatically create ANNs.

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Acknowledgements

We are grateful to the National Council for Science and Technology (CONACYT) of Mexico for the support provided by means of the Scholarship for Postgraduate Studies: 703036 (O. Quiroz) and Research Grant: CÁTEDRAS-2598 (A. Rojas) as well as to the National Technology Institute of Mexico.

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Authors and Affiliations

  1. Tecnológico Nacional de México-Instituto Tecnológico de León, León, Mexico

    Olga Quiroz-Ramírez, Manuel Ornelas-Rodríguez, Alfonso Rojas-Domínguez, Héctor Puga-Soberanes, Martin Carpio & Luis Ernesto Mancilla Espinoza

  2. División de Ciencias Económico Administrativas, Universidad de Guanajuato, Guanajuato, Mexico

    Andrés Espinal

  3. Tecnológico Nacional de México-Instituto Tecnológico de Tijuana, Tijuana, Mexico

    Daniela Sánchez

  4. Escuela Internacional de Doctorado, Universidad de Vigo, Vigo, Spain

    Janet Ortíz-López

Authors
  1. Olga Quiroz-Ramírez
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  2. Andrés Espinal
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  3. Manuel Ornelas-Rodríguez
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  4. Alfonso Rojas-Domínguez
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  5. Daniela Sánchez
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  6. Héctor Puga-Soberanes
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  7. Martin Carpio
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  8. Luis Ernesto Mancilla Espinoza
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  9. Janet Ortíz-López
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Correspondence to Andrés Espinal .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research , Tijuana Institute of Technology , Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences , Warszawa, Poland

    Janusz Kacprzyk

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Quiroz-Ramírez, O. et al. (2018). Partially-Connected Artificial Neural Networks Developed by Grammatical Evolution for Pattern Recognition Problems. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-71008-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71007-5

  • Online ISBN: 978-3-319-71008-2

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