An evolutionary genetic neural networks for problems without prior knowledge

Hyoung Uk Ha, Jong-Kook Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Many problems are now being solved using a version of a neural network (NN). These NN are usually constructed using genetic neural networks (GNNs) for optimizing variables in the NN using a fixed structure or neural evolution (NE) to optimize the structure of the NN using fixed values for the variables in the NN. Thus, previous methods need experienced knowledge of the problem such that either the structure or variables are known to construct a meaningful NN. This paper presents a method called leap evolution adopted neural network (LEANN) that optimizes the NN without prior knowledge such as the values of the variables and the structure of the NN for a given problem. Our method in this paper finds an optimal structure and variables of the NN successfully for the XOR gate problem.

Original languageEnglish
Title of host publication2014 10th International Conference on Natural Computation, ICNC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Print)9781479951505
DOIs
Publication statusPublished - 2014
Event2014 10th International Conference on Natural Computation, ICNC 2014 - Xiamen, China
Duration: 2014 Aug 192014 Aug 21

Other

Other2014 10th International Conference on Natural Computation, ICNC 2014
CountryChina
CityXiamen
Period14/8/1914/8/21

Keywords

  • Bio-inspired algorithm
  • Evolutionary algorithm
  • Genetic algorithm
  • Genetic neural network
  • Multilayer perceptron
  • Neuro-evolution

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering

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