An Effective Hybrid Approach for Optimising the Learning Process of Multi-layer Neural Networks

  • Seyed Jalaleddin Mousavirad
  • Azam Asilian Bidgoli
  • Hossein Ebrahimpour-Komleh
  • Gerald SchaeferEmail author
  • Iakov Korovin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)


Finding the optimal connection weights in a neural network is one of the most challenging tasks in machine learning and pattern recognition. The main disadvantage of conventionally used algorithms such as back-propagation is that they show a tendency of getting trapped in local rather than global optima. To address this, population-based metaheuristic algorithms can be employed. In this paper, we propose a novel approach to optimise the weights of a neural network. Our method integrates an imperialist competitive algorithm, a powerful metaheuristic algorithm, with chaos theory and back-propagation for neural network learning. Experimental results on the three-bit parity problem and several function approximation tasks confirm that our proposed algorithm significantly outperforms several state-of-the-art methods for neural network weight optimisation.


Machine learning Neural networks Weight optimisation Imperialist competitive algorithm Chaos theory Back-propagation 



The paper is published due to the financial support of the Ministry of Science and Higher Education of Russia, contract 14.575.21.0152, signed 26/09/2017, unique identifier RFMEFI57517X0152.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Seyed Jalaleddin Mousavirad
    • 1
  • Azam Asilian Bidgoli
    • 1
  • Hossein Ebrahimpour-Komleh
    • 1
  • Gerald Schaefer
    • 2
    Email author
  • Iakov Korovin
    • 3
  1. 1.Department of Computer and Electrical EngineeringUniversity of KashanKashanIran
  2. 2.Department of Computer ScienceLoughborough UniversityLoughboroughUK
  3. 3.Southern Federal UniversityTaganrogRussia

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