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Automatic Design of Radial Basis Function Networks Through Enhanced Differential Evolution

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Hybrid Artificial Intelligent Systems (HAIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9121))

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Abstract

During the creation of a classification model, it is vital to keep track of numerous parameters and to produce a model based on the limited knowledge inferred often from very confined data. Methods which aid the construction or completely build the classification model automatically, present a fairly common research interest. This paper proposes an approach that employs differential evolution enhanced through the incorporation of additional knowledge concerning the problem in order to design a radial basis neural network. The knowledge is inferred from the unsupervised learning procedure which aims to ensure an initial population of good solutions. Also, the search space is dynamically adjusted i.e. narrowed during runtime in terms of the decision variables count. The results obtained on several datasets suggest that the proposed approach is able to find well performing networks while keeping the structure simple. Furthermore, a comparison with a differential evolution algorithm without the proposed enhancements and a particle swarm optimization algorithm was carried out illustrating the benefits of the proposed approach.

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Acknowledgments

This work was supported by research project grant No. 165-0362980-2002 from the Ministry of Science, Education and Sports of the Republic of Croatia.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, J.J. Strossmayer University of Osijek, Kneza Trpimira 2b, 31000, Osijek, Croatia

    Dražen Bajer, Bruno Zorić & Goran Martinović

Authors
  1. Dražen Bajer
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  2. Bruno Zorić
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  3. Goran Martinović
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Corresponding author

Correspondence to Goran Martinović .

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Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Enrique Onieva

  2. University of Deusto, Bilbao, Spain

    Igor Santos

  3. University of Deusto, Bilbao, Spain

    Eneko Osaba

  4. Universidad de Salamanca, Salamanca, Spain

    Héctor Quintián

  5. Universidad de Salamanca, Salamanca, Spain

    Emilio Corchado

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Bajer, D., Zorić, B., Martinović, G. (2015). Automatic Design of Radial Basis Function Networks Through Enhanced Differential Evolution. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-19644-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19643-5

  • Online ISBN: 978-3-319-19644-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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