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Hands-On Neuroevolution with Python

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

Omelianenko

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Paperback Dec 2019 368 pages 1st Edition

₱1399.99 ~~₱2000.99~~

Omelianenko

Free Trial

Paperback Dec 2019 368 pages 1st Edition

₱1399.99 ~~₱2000.99~~

₱1399.99 ~~₱2000.99~~

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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.

Who is this book for?

This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.

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

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Product Details

Publication date : Dec 24, 2019

Length: 368 pages

Edition : 1st

Language : English

ISBN-13 : 9781838824914

Category :

Data

Languages :

Concepts :

Neural Networks

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Table of Contents

17 Chapters

Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods

Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods

Overview of Neuroevolution Methods

Overview of Neuroevolution Methods

Evolutionary algorithms and neuroevolution-based methods

NEAT algorithm overview

Hypercube-based NEAT

Evolvable-Substrate HyperNEAT

Novelty Search optimization method

Further reading

Python Libraries and Environment Setup

Python Libraries and Environment Setup

Suitable Python libraries for neuroevolution experiments

Environment setup

Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems

Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems

Using NEAT for XOR Solver Optimization

Using NEAT for XOR Solver Optimization

Technical requirements

XOR problem basics

The objective function for the XOR experiment

Hyperparameter selection

Running the XOR experiment

Pole-Balancing Experiments

Pole-Balancing Experiments

Technical requirements

The single-pole balancing problem

Objective function for a single-pole balancing experiment

The single-pole balancing experiment

The double-pole balancing problem

Objective function for a double-pole balancing experiment

Double-pole balancing experiment

Autonomous Maze Navigation

Autonomous Maze Navigation

Technical requirements

Maze navigation problem

Maze simulation environment

Objective function definition using the fitness score

Running the experiment with a simple maze configuration

Running the experiment with a hard-to-solve maze configuration

Novelty Search Optimization Method

Novelty Search Optimization Method

Technical requirements

The NS optimization method

NS implementation basics

The fitness function with the novelty score

Experimenting with a simple maze configuration

Experimenting with a hard-to-solve maze configuration

Section 3: Advanced Neuroevolution Methods

Section 3: Advanced Neuroevolution Methods

Hypercube-Based NEAT for Visual Discrimination

Hypercube-Based NEAT for Visual Discrimination

Technical requirements

Indirect encoding of ANNs with CPPNs

Visual discrimination experiment basics

Visual discrimination experiment setup

Visual discrimination experiment

ES-HyperNEAT and the Retina Problem

ES-HyperNEAT and the Retina Problem

Technical requirements

Manual versus evolution-based configuration of the topography of neural nodes

Quadtree information extraction and ES-HyperNEAT basics

Modular retina problem basics

Modular retina experiment setup

Modular retina experiment

Co-Evolution and the SAFE Method

Co-Evolution and the SAFE Method

Technical requirements

Common co-evolution strategies

Modified maze experiment

Modified Novelty Search

Modified maze experiment implementation

Modified maze experiment

Deep Neuroevolution

Deep Neuroevolution

Technical requirements

Deep neuroevolution for deep reinforcement learning

Evolving an agent to play the Frostbite Atari game using deep neuroevolution

Training an agent to play the Frostbite game

Running the Frostbite Atari experiment

Visual inspector for neuroevolution

Section 4: Discussion and Concluding Remarks

Section 4: Discussion and Concluding Remarks

Best Practices, Tips, and Tricks

Best Practices, Tips, and Tricks

Starting with problem analysis

Selecting the optimal search optimization method

Advanced visualization

Tuning hyperparameters

Performance metrics

Python coding tips and tricks

Concluding Remarks

Concluding Remarks

What we learned in this book

Where to go from here

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Customer reviews

Rating distribution

3

(1 Ratings)

5 star 0%

4 star 0%

3 star 100%

2 star 0%

1 star 0%

Rohitashwa Kumar Jun 09, 2020

3

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.

Amazon Verified review

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About the author

Omelianenko

Omelianenko

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.

See other products by Omelianenko

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