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Evolving Connection Weights of Artificial Neural Network Using a Multi-Objective Approach with Application to Class Prediction

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Designing with Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 664))

Abstract

In Artificial Neural Network (ANN), the selection of connection weights is a key issue and Genetic and Evolution Strategies have been found to be promising algorithms to solve this important task. Motivated by that, this study investigates the applicability of using two novel Multi-Objective Evolutionary Algorithms (MOEA): Speed constrained Multi-Objective Particle Swarm Optimization (SMPSO) and Multi-Objective Differential Evolution Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DE-DRA). ANNs are training to learn data classification using sensibility and specificity for different UCI databases. The results are compared using the Hypervolume as quality indicator and statistical test.

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Acknowledgments

Authors would like to thank CNPq and CAPES for financial support.

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Authors and Affiliations

  1. Computer Science Department, Federal University of Paraná, Curitiba, Brazil

    Andrei Strickler & Aurora Pozo

Authors
  1. Andrei Strickler
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  2. Aurora Pozo
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Corresponding author

Correspondence to Andrei Strickler .

Editor information

Editors and Affiliations

  1. Electronics Engg & Telecomm, State Univ of Rio de Janeiro Electronics Engg & Telecomm, Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah

  2. Department of Electronics, Tech. Fed. Uni. of Para - UTFPR Department of Electronics, Curitiba-PR, Brazil

    Heitor Silvério Lopes

  3. Dept. of Sys. Eng. & Comp., State Univ. of Rio de Janeiro - UERJ Dept. of Sys. Eng. & Comp., Rio de Janeiro-RJ, Brazil

    Luiza de Macedo Mourelle

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Strickler, A., Pozo, A. (2017). Evolving Connection Weights of Artificial Neural Network Using a Multi-Objective Approach with Application to Class Prediction. In: Nedjah, N., Lopes, H., Mourelle, L. (eds) Designing with Computational Intelligence. Studies in Computational Intelligence, vol 664. Springer, Cham. https://doi.org/10.1007/978-3-319-44735-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-44735-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44734-6

  • Online ISBN: 978-3-319-44735-3

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