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Recent Advances on Hybrid Intelligent Systems pp 107–120Cite as

Multi-Objective Hierarchical Genetic Algorithm for Modular Neural Network Optimization Using a Granular Approach

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 451))

Abstract

In this paper we propose a multi-objective hierarchical genetic algorithm (MOHGA) for modular neural network optimization. A granular approach is used due to the fact that the dataset is divided into granules or sub modules. The main objective of this method is to know the optimal number of sub modules or granules, but also allow the optimization of the number of hidden layers, number of neurons per hidden layer, error goal and learning algorithms per module. The proposed MOHGA is based on the Micro genetic algorithm and was tested for a pattern recognition application. Simulation results show that the proposed modular neural network approach offers advantages over existing neural network models.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, México

    Daniela Sánchez & Patricia Melin

Authors
  1. Daniela Sánchez
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  2. Patricia Melin
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Corresponding author

Correspondence to Daniela Sánchez .

Editor information

Editors and Affiliations

  1. Chula Vista, 91909, California, USA

    Oscar Castillo

  2. Chula Vista, 91909, California, USA

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warszawa, 01-447, Poland

    Janusz Kacprzyk

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Sánchez, D., Melin, P. (2013). Multi-Objective Hierarchical Genetic Algorithm for Modular Neural Network Optimization Using a Granular Approach. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_9

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  • DOI: https://doi.org/10.1007/978-3-642-33021-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33020-9

  • Online ISBN: 978-3-642-33021-6

  • eBook Packages: EngineeringEngineering (R0)

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