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An Evolutionary Approach to Hyper-Parameter Optimization of Neural Networks

  • Marco StangEmail author
  • Christopher Meier
  • Vinzenz Rau
  • Eric Sax
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1018)

Abstract

The last few years have increasingly been influenced by the emergence of neural networks for classification tasks. Due to the increasing size of the nets, especially concerning deep neural nets, the selection of the optimal topology becomes critical. A set of hyper-parameters determines the topology of a neural network. The selection of suitable hyper-parameters however, is time-consuming or requires expert knowledge. This paper examines the application of an evolutionary algorithm to optimize the hyper-parameters of a neural network. The result of the optimization by evolutionary algorithms can be applied for a variety of use-cases: i.e. detection of anomalies in CAN-bus data, object recognition for autonomous driving or the classification of hydraulic machinery oil.

Keywords

Machine learning Hyper-parameter optimization Neural network Evolutionary algorithm 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marco Stang
    • 1
    Email author
  • Christopher Meier
    • 1
  • Vinzenz Rau
    • 1
  • Eric Sax
    • 1
  1. 1.Institut für Technik der Informationsverarbeitung (ITIV)KarlsruheGermany

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