10 NEAT: NeuroEvolution of Augmenting Topologies

 

This chapter covers

  • Building evolving augmenting topological networks
  • Visualizing a NEAT network
  • Exercising the capabilities of NEAT
  • Exercising NEAT to classify images
  • Uncovering the role of speciation in Neuroevolution

Over the course of the last couple of chapters, we explored the evolutionary optimization of generative adversarial and autoencoder networks. Much like our previous chapters in those exercises we layered or wrapped evolutionary optimization around deep learning networks. In this chapter, we break from DEAP and Keras to explore a neuroevolutionary framework called NEAT.

Neuroevolution of augmenting topologies (NEAT) was developed by Ken Stanley in 2002 while at the University of Texas at Austin. At the time genetic algorithms (evolutionary computation) and deep learning (advanced neural networks) were equals and both were considered the next big thing in AI. Stanley’s NEAT framework captured the attention of many because it combined neural networks with evolution to not just optimize hyperparameters, weight parameters, and architecture; but the actual neural connections themselves.

Figure 10.1 shows a comparison between a regular DL network and an evolved NEAT network. In the figure, new connections have been added/removed and the position of a node removed and/or altered in the evolved NEAT network. Notice how this differs from our previous efforts of simply altering the number of nodes in a DL-connected layer.

10.1 Exploring NEAT with NEAT-Python

 
 
 
 

10.1.1 Learning Exercises

 

10.2 Visualizing an Evolved NEAT Network

 
 

10.3 Exercising the Capabilities of NEAT

 
 
 

10.3.1 Learning Exercises

 

10.4 Exercising NEAT to Classify Images

 
 
 

10.4.1 Learning Exercises

 
 

10.5 Uncovering the Role of Speciation in Evolving Topologies

 
 
 

10.5.1 Tuning NEAT Speciation

 
 

10.5.2 Learning Exercises

 

10.6 Summary

 
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