In this chapter, you will learn about the ES-HyperNEAT extension of the HyperNEAT method, which we discussed in the previous chapter. As you learned in the previous chapter, the HyperNEAT method allows the encoding of larger-scale artificial neural network (ANN) topologies, which is essential for working in areas where the input data has a large number of dimensions, such as computer vision. However, despite all its power, the HyperNEAT method has a significant drawback—the configuration of the ANN substrate should be designed beforehand by a human architect. The ES-HyperNEAT method was invented to address this issue by introducing the concept of evolvable-substrate, which allows us to produce the appropriate configuration of the substrate automatically during evolution.
After familiarizing yourself with the basics of the ES-HyperNEAT...
