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Hands-On Neuroevolution with Python

Hands-On Neuroevolution with Python

By : Omelianenko
3 (1)
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Hands-On Neuroevolution with Python

Hands-On Neuroevolution with Python

3 (1)
By: Omelianenko

Overview of this book

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.
Table of Contents (18 chapters)
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Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods
4
Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems
9
Section 3: Advanced Neuroevolution Methods
14
Section 4: Discussion and Concluding Remarks

Python Libraries and Environment Setup

This chapter introduces the Python libraries that we can use in order to implement the neuroevolution algorithms we described in the previous chapter. We will also discuss the strengths and weaknesses of each library that's presented. In addition to this, we will provide basic usage examples. Then, we will consider how to set up the environment for the experiments that we will perform later in this book and examine common ways to do this in the Python ecosystem. Finally, we will demonstrate how to set up a working environment using Anaconda Distribution, which is a popular tool for managing Python dependencies and virtual environments among data scientists. In this chapter, you will learn how to start using Python to experiment with the neuroevolution algorithms that will be covered in this book.

In this chapter, we will cover the following...

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