Understanding Compositional Pattern Producing Networks (Part One) | by Cameron R. Wolfe, Ph.D. | Towards Data Science

For the last two years, I have been researching Compositional Pattern Producing Networks (CPPN), a type of augmenting topology neural network proposed in [1]. However, throughout my research, I was always baffled by some of the concepts and theory behind CPPNs, and I struggled to understand how they work. Although a few open source versions of CPPN exist online, I wanted to build my own implementation for customization purposes in my research, which turned out to be significantly more involved than I initially expected. So, I wanted to create a comprehensive explanation of the theory behind CPPNs and how they can be implemented, so that such a resource is available to everyone that struggles with CPPN just as I did. This article will be broken up into two separate parts. Part one, which you are reading right now, will focus on the theory behind CPPNs and how they have been used in scientific research, while part two will focus on actually implementing a CPPN in Python. Proposed in [1],

For the last two years, I have been researching Compositional Pattern Producing Networks (CPPN), a type of augmenting topology neural network proposed in [1]. However, throughout my research, I was always baffled by some of the concepts and theory behind CPPNs, and I struggled to understand how they work. Although a few open source versions of CPPN exist online, I wanted to build my own implementation for customization purposes in my research, which turned out to be significantly more involved than I initially expected. So, I wanted to create a comprehensive explanation of the theory behind CP