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Train RBF networks with a hybrid genetic algorithm

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Abstract

RBF neural networks are well established tools for classification and regression problems. This article adapts a hybrid genetic algorithm to estimate the main parameters of the network. The proposed method utilizes a genetic algorithm in a conjunction with a local search procedure and a termination rule. The method is tested against other RBF variants on a series of well-known problems from the relevant literature and the results are reported.

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Acknowledgements

The experiments of this research work was performed at the high performance computing system established at Knowledge and Intelligent Computing Lab-oratory, Dept of Informatics and Telecommunications, University of Ioannina, acquired with the project “Educational Laboratory equipment of TEI of Epirus” with MIS 5007094 funded by the Operational Programme “Epirus” 2014–2020, by ERDF and national finds.

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Authors and Affiliations

  1. Department of Informatics and Telecommunications, University of Ioannina, Ioannina, Greece

    Ioannis G. Tsoulos, Georgios Ntritsos & Alexandros Tzallas

  2. Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

    Nikolaos Anastasopoulos

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  1. Ioannis G. Tsoulos
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  2. Nikolaos Anastasopoulos
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  3. Georgios Ntritsos
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  4. Alexandros Tzallas
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Correspondence to Ioannis G. Tsoulos.

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Tsoulos, I.G., Anastasopoulos, N., Ntritsos, G. et al. Train RBF networks with a hybrid genetic algorithm. Evol. Intel. 16, 375–381 (2023). https://doi.org/10.1007/s12065-021-00654-2

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