Hands-On Neuroevolution with Python

▶Book Description
Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems.

You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones.

By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments.

▶What You Will Learn
- Discover the most popular neuroevolution algorithms – NEAT, HyperNEAT, and ES-HyperNEAT
- Explore how to implement neuroevolution-based algorithms in Python
- Get up to speed with advanced visualization tools to examine evolved neural network graphs
- Understand how to examine the results of experiments and analyze algorithm performance
- Delve into neuroevolution techniques to improve the performance of existing methods
- Apply deep neuroevolution to develop agents for playing Atari games

▶Key Features
- Implement neuroevolution algorithms to improve the performance of neural network architectures
- Understand evolutionary algorithms and neuroevolution methods with real-world examples
- Learn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulations

▶Who This Book Is For
This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.

▶What this book covers
- 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.