This is the code repository for Hands-On Neuroevolution with Python , published by Packt.

Build high-performing artificial neural network architectures using neuroevolution-based algorithms

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.

This book covers the following exciting features:

• 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

If you feel this book is for you, get your copy today!

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

for xi in xor_inputs:
    output = winner_ann.activate(xi)
    print(xi, output) # print results

Following is what you need for this book: A practical knowledge of the Python programming language is essential to work with the examples presented in this book. For better source code understanding, it is preferable to use an IDE that supports Python syntax highlighting and code reference location. If you don't have one installed, you can use Microsoft Visual Studio Code. It is free and cross-platform, and you can download it here: https://code.visualstudio.com.

With the following software and hardware list you can run all code files present in the book (Chapter 1-15).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Hands-On Deep Learning Algorithms with Python [Packt] [Amazon]

• Python Deep Learning - Second Edition [Packt] [Amazon]

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.

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