Chapter 1, Overview of Neuroevolution Methods, introduces the core concepts of genetic algorithms, such as genetic operators and genome encoding schemes.
Chapter 2, Python Libraries and Environment Setup, discusses the practical aspects of neuroevolution methods. This chapter provides the pros and cons of popular Python libraries that provide implementations of the NEAT algorithm and its extensions.
Chapter 3, Using NEAT for XOR Solver Optimization, is where you start experimenting with the NEAT algorithm by implementing a solver for a classical computer science problem.
Chapter 4, Pole-Balancing Experiments, is where you continue with experiments related to the classic problems of computer science in the field of reinforcement learning.
Chapter 5, Autonomous Maze Navigation, is where you continue your experiments with neuroevolution through an attempt to create a solver that can find an exit from a maze. You will learn how to implement a simulation of a robot that has an array of sensors to detect obstacles and monitor its position within the maze.
Chapter 6, Novelty Search Optimization Method, is where you use the practical experience gained during the creation of a maze solver in the previous chapter to embark on the path of creating a more advanced solver.
Chapter 7, Hypercube-Based NEAT for Visual Discrimination, introduces you to advanced neuroevolution methods. You'll learn about the indirect genome encoding scheme, which uses Compositional Pattern Producing Networks (CPPNs) to aid with the encoding of large-phenotype ANN topologies.
Chapter 8, ES-HyperNEAT and the Retina Problem, is where you will learn how to select the substrate configuration that is best suited for a specific problem space.
Chapter 9, Co-Evolution and the SAFE Method, is where we discuss how a co-evolution strategy is widely found in nature and could be transferred into the realm of the neuroevolution.
Chapter 10, Deep Neuroevolution, presents you with the concept of Deep Neuroevolution, which can be used to train Deep Artificial Neural Networks (DNNs).
Chapter 11, Best Practices, Tips, and Tricks, teaches you how to start working with whatever problem is at hand, how to tune the hyperparameters of a neuroevolution algorithm, how to use advanced visualization tools, and what metrics can be used for the analysis of algorithm performance.
Chapter 12, Concluding Remarks, summarizes everything you have learned in this book and provides further directions for you to continue your self-education.
