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Multi-layer Perceptron Training Using Hybridized Bat Algorithm

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Computational Vision and Bio-Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1318))

Abstract

Neural networks (NNs) are a subset in the field of machine learning (ML) that tends to make it possible for a machine to learn and make new predictions based on previous experiences and provided data. It is important to emphasize that there is no need to program this kind of behavior since the whole process is supported by the “self-adjustment” of the algorithm, which can evaluate itself and therefore adjust its parameters to get better performance and accuracy. Neural networks are different from other types of machine learning algorithms in such a way that they do not use statistical and mathematical models to make future predictions. Instead, they replicate the structure and the processes that happen inside the human brain. However, this type of learning is very computationally expensive since there is an enormous amount of states and conditions in which the network itself can be found. Therefore, it can be said that a process of learning for neural networks relates to the collection of NP-complete problems because of the large search space of possible solutions. Swarm intelligence (SI) algorithms can help to reduce this large space of solutions by finding a solution that is not optimal but close optimal and provide satisfactory results given how much longer it would take to train a network without them. In this paper, the authors have proposed a solution for the stated problem based on hybridized bat algorithm.

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Author information

Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Luka Gajic, Dusan Cvetnic, Miodrag Zivkovic, Timea Bezdan, Nebojsa Bacanin & Stefan Milosevic

Authors
  1. Luka Gajic
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  2. Dusan Cvetnic
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  3. Miodrag Zivkovic
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  4. Timea Bezdan
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  5. Nebojsa Bacanin
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  6. Stefan Milosevic
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Corresponding author

Correspondence to Luka Gajic .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Departamento de Engenharia Mecanica, SDI, Faculty of Engineering, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Czech Technical University in Prague, Prague, Czech Republic

    Robert Bestak

  4. College of Graduate Studies, University of Central Florida, Orlando, FL, USA

    Fuqian Shi

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Gajic, L., Cvetnic, D., Zivkovic, M., Bezdan, T., Bacanin, N., Milosevic, S. (2021). Multi-layer Perceptron Training Using Hybridized Bat Algorithm. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_54

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