7 Evolutionary convolutional neural networks

This chapter covers

  • Convolutional neural networks with a Keras primer
  • Defining a neural network architecture with a gene sequence
  • Building a custom crossover operator
  • Applying a custom mutation operator
  • Evolving the best convolutional network architecture for a given dataset

The last chapter showed us the limits of evolutionary algorithms when applied to a complex problem like parameter search. As we have seen, genetic algorithms can provide excellent results on a certain class of problems. However, they fail to deliver when employed for larger image classification networks.

In this chapter, we continue looking at larger networks for image classification. However, this time instead of optimizing ...

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