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Evolutionary Computation Applied to the Automatic Design of Artificial Neural Networks and Associative Memories

  • Humberto Sossa
  • Beatriz A. Garro
  • Juan Villegas
  • Gustavo Olague
  • Carlos Avilés
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)

Abstract

In this paper we describe how evolutionary computation can be used to automatically design artificial neural networks (ANNs) and associative memories (AMs). In the case of ANNs, Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithms are used, while Genetic Programming is adopted for AMs. The derived ANNs and AMs are tested with several examples of well-known databases.

Keywords

Particle Swarm Optimization Genetic Programming Differential Evolution Automatic Design Synaptic Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Humberto Sossa
    • 1
  • Beatriz A. Garro
    • 1
  • Juan Villegas
    • 2
  • Gustavo Olague
    • 3
  • Carlos Avilés
    • 2
  1. 1.CIC-IPNMexico CityMexico
  2. 2.UAM-AzcapotzalcoMexico CityMexico
  3. 3.CICESE, Carretera Ensenada-TijuanaEnsenadaMexico

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