Hands-On Neuroevolution with Python

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.

• 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

Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.

1. Overview of Neuroevolution Methods
2. Python Libraries and Environment Setup
3. Using NEAT for XOR Solver Optimization
4. Pole-Balancing Experiments
5. Autonomous Maze Navigation
6. Novelty Search Optimization Method
7. Hypercube-Based NEAT for Visual Discrimination
8. ES-HyperNEAT and the Retina Problem
9. Co-Evolution and the SAFE Method
10. Deep Neuroevolution
11. Best Practices, Tips, and Tricks
12. Concluding Remarks