Evolutionary Associative Memories through Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Juan Villegas-Cortez and Gustavo Olague and 
                 Humberto Sossa and Carlos Aviles",
  title =        "Evolutionary Associative Memories through Genetic
  booktitle =    "Parallel Architectures and Bioinspired Algorithms",
  publisher =    "Springer",
  year =         "2012",
  editor =       "Francisco {Fernandez de Vega} and 
                 Jose Ignacio {Hidalgo Perez} and Juan Lanchares",
  volume =       "415",
  series =       "Studies in Computational Intelligence",
  chapter =      "7",
  pages =        "171--188",
  keywords =     "genetic algorithms, genetic programming, coevolution",
  isbn13 =       "978-3-642-28788-6",
  URL =          "http://www.amazon.com/Architectures-Bioinspired-Algorithms-Computational-Intelligence/dp/3642287883",
  DOI =          "doi:10.1007/978-3-642-28789-3_8",
  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 (AMs) and
                 which are considered as a practical tool for reaching
                 learning tasks in pattern recognition problems. AMs are
                 complex operators, based on simple arithmetical
                 functions, which are used to recall patterns in terms
                 of some input data. AMs 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 AMs 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.",
  affiliation =  "Departamento de Electronica, Universidad Autonoma
                 Metropolitana - Azcapotzalco, Av. San Pablo 180 Col.
                 Reynosa, 02200 Mexico D.F., Mexico",

Genetic Programming entries for Juan Villegas-Cortez Gustavo Olague Juan Humberto Sossa Azuela Carlos Aviles-Cruz