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Overview of Neuroevolution Methods

The concept of artificial neural networks (ANN) was inspired by the structure of the human brain. There was a strong belief that, if we were able to imitate this intricate structure in a very similar way, we would be able to create artificial intelligence. We are still on the road to achieving this. Although we can implement Narrow AI agents, we are still far from creating a Generic AI agent.

This chapter introduces you to the concept of ANNs and the two methods that we can use to train them (the gradient descent with error backpropagation and neuroevolution) so that they learn how to approximate the objective function. However, we will mainly focus on discussing the neuroevolution-based family of algorithms. You will learn about the implementation of the evolutionary process that's inspired by natural evolution and become familiar with...

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 Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

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DRM FREE - Read whenever, wherever and however you want

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Rohitashwa Kumar Jun 09, 2020

Buy this book for the GitHub links to the codes only. Apart from that the books only tells about setting up virtual environment on the computer for all the codes. Codes themselves are not explained very well. What's written in the book one can figure out by just looking at the codes. That's the beauty of Python language. And what needs to be explained is not there in this book. There is a brief comparison of different implementations but that's too brief and leaves too many questions unanswered.

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Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms

What do you get with Print?

Hands-On Neuroevolution with Python

Overview of Neuroevolution Methods

Evolutionary algorithms and neuroevolution-based methods

NEAT algorithm overview

Hypercube-based NEAT

Evolvable-Substrate HyperNEAT

Novelty Search optimization method

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