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This section introduces core concepts of evolutionary computation and discusses particulars of neuroevolution-based algorithms and which Python libraries can be used to implement them. You will become familiar with the fundamentals of neuroevolution methods and will get practical recommendations on how to start your experiments. This section provides a basic introduction to the Anaconda package manager for Python as part of your environment setup.

This section comprises the following chapters:

Chapter 1, Overview of Neuroevolution Methods
Chapter 2, Python Libraries and Environment Setup

Key benefits

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

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

What do you get with eBook?