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Evolutionary Associative Memories through Genetic Programming

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Parallel Architectures and Bioinspired Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 415))

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Abstract

Natural systems apply learning during the process of adaptation, as a way of developing strategies that help to succeed them in highly complex scenarios. In particular, it is said that the plans developed by natural systems are seen as a fundamental aspect in survival. Today, there is a huge interest in attempting to replicate some of their characteristics by imitating the processes of evolution and genetics in artificial systems using the very well-known ideas of evolutionary computing. For example, some models for learning adaptive process are based on the emulation of neural networks that are further evolved by the application of an evolutionary algorithm. In this work, we present the evolution of a kind of neural network that is collectible known as associative memories (AM’s) and which are considered as a practical tool for reaching learning tasks in pattern recognition problems. AM’s are complex operators, based on simple arithmetical functions, which are used to recall patterns in terms of some input data. AM’s are considered as part of artificial neural networks (ANN), mainly due to its primary conception; nevertheless, the idea inherent to their mathematical formulation provides a powerful description that helps to reach a specific goal despite the numerous changes that can happen during its operation. In this chapter, we describe the idea of building new AM’s through genetic programming (GP) based on the coevolutionary paradigm. The methodology that is proposed consists in splitting the problem in two populations that are used to evolve simultaneously both processes of association and recall that are commonly used in AM’s. Experimental results on binary and real value patterns are provided in order to illustrate the benefits of applying the paradigm of evolutionary computing to the synthesis of associative memories.

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Author information

Authors and Affiliations

  1. Departamento de Electrónica, Universidad Autónoma Metropolitana - Azcapotzalco, Av. San Pablo 180 Col. Reynosa, 02200, México D.F., México

    Juan Villegas-Cortez & Carlos Avilés

  2. Centro de Investigación Científica y de Educación Superior de Ensenada, CICESE, Ensenada, B.C., México

    Gustavo Olague

  3. Centro de Investigación en Computación CIC-IPN, México D.F., México

    Humberto Sossa

Authors
  1. Juan Villegas-Cortez
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  2. Gustavo Olague
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  3. Humberto Sossa
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  4. Carlos Avilés
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Corresponding author

Correspondence to Juan Villegas-Cortez .

Editor information

Editors and Affiliations

  1. Centro Universitario de Mérida, Universidad de Extremadura, C/ Sta Teresa de Jornet 38, Mérida, 06800, Spain

    Francisco Fernández de Vega

  2. Facultad de Informática, Universidad Complutense de Madrid, Calle del Profesor José García santesmases s/n, Madrid, 28040, Spain

    José Ignacio Hidalgo Pérez

  3. Facultad de Informática, Universidad Complutense de Madrid, Calle del Profesor José García santesmases s/n, Madrid, 28040, Spain

    Juan Lanchares

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Villegas-Cortez, J., Olague, G., Sossa, H., Avilés, C. (2012). Evolutionary Associative Memories through Genetic Programming. In: Fernández de Vega, F., Hidalgo Pérez, J., Lanchares, J. (eds) Parallel Architectures and Bioinspired Algorithms. Studies in Computational Intelligence, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28789-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-28789-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28788-6

  • Online ISBN: 978-3-642-28789-3

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