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Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers

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Frontier Applications of Nature Inspired Computation

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Artificial neural networks (ANN) have a great impact on research in the field of artificial intelligence. It has great capability besides the easy implementation, and due to that, it has been widely used in a wide area of real-life and industrial applications. Today, we can see a variety of ANNs such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, spiking ANN, etc. This chapter focuses on evolutionary ANN wherein the learning process is by nature-inspired optimization techniques instead of the classic routine. The focus of this chapter is the neuro-evolution-based ANN techniques by different state-of-the-art nature-inspired meta-heuristic optimization techniques and comparison of them over a monitoring system to detect the oil filter condition in agricultural machines (Ag machines). In this comparative study, the fourteen state-of-art meta-heuristic optimizers are compared in the same regard.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Neeraj Gupta & Nilesh Patel

  2. Media Integrated Communication Lab, Graduate School of Engineering, Osaka University, Suita, Japan

    Mahdi Khosravy

  3. Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Mahdi Khosravy

  4. Department of Advanced Engineering, John Deere India Pvt. Ltd., Pune, India

    Saurabh Gupta

  5. Research Scholar, Department of Computer Science, Banasthali Vidyapith, Vanasthali, Rajasthan, India

    Saurabh Gupta

  6. University of Information Science and Technology, Ohrid, North Macedonia

    Gazal Varshney

Authors
  1. Neeraj Gupta
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  2. Mahdi Khosravy
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  3. Nilesh Patel
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  4. Saurabh Gupta
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  5. Gazal Varshney
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Corresponding author

Correspondence to Mahdi Khosravy .

Editor information

Editors and Affiliations

  1. Media Integrated Communication Lab, Graduate School of Engineering, Osaka University, Osaka, Japan

    Mahdi Khosravy

  2. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Neeraj Gupta

  3. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Nilesh Patel

  4. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Nishihara, Japan

    Tomonobu Senjyu

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Gupta, N., Khosravy, M., Patel, N., Gupta, S., Varshney, G. (2020). Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers. In: Khosravy, M., Gupta, N., Patel, N., Senjyu, T. (eds) Frontier Applications of Nature Inspired Computation. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-2133-1_14

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