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Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms
Authors: Iaroslav Omelianenko
ISBN-10 书号: 183882491X
ISBN-13 书号: 9781838824914
Publisher Finelybook 出版日期: 2019-12-24
pages 页数: 368 pages
Book Description
Increase the performance of various neural network architectures using NEAT,HyperNEAT,ES-HyperNEAT,Novelty Search,SAFE,and Deep Neuroevolution.
Neuroevolution is a form of artificial intelligence learning method that uses evolutionary algorithms to ease the solving of complex tasks such as games,robotics,simulation of natural processes,etc. This book serves as a practical guide on how to develop the necessary mindset and skills to apply neuroevolution-based algorithms to solve practical,real-world problems.
You will learn the key concepts and methods of neuroevolution by writing code with Python programming language. You will get hands-on experience with popular Python libraries and will cover examples of classical reinforcement learning,path planning for autonomous agents,creation of agents to autonomously play Atari games,and other real-world examples. You will learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. You will learn to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. You will get a clear understanding of the topology of the neural network and how neuroevolution allows you to evolve complex networks,starting with the very simple ones.
By the end of this book,you will not only have studied existing neuroevolution-based algorithms but also built the practical skills necessary to apply it for research and work assignments.
What you will learn
Learn about most prominent neuroevolution algorithms – NEAT,HyperNEAT,and the ESHyperNEAT
Explore how to implement neuroevolution-based algorithms in Python
Understand advanced visualization tools to examine graphs of evolved neural networks
Learn how to examine experiment results and analyze the performance of algorithms
Explore neuroevolution techniques to improve the performance of existing methods
Discover how to search for solutions without directly aiming at the objective
Apply deep neuroevolution to evolve agents to play Atari games
Contents
Preface
Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution
Methods
Chapter 1: Overview of Neuroevolution Methods
Chapter 2: Python Libraries and Environment Setup
Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science
Problems
Chapter 3: Using NEAT for XOR Solver Optimization
Chapter 4: Pole-Balancing Experiments
Chapter 5: Autonomous Maze Navigation
Chapter 6: Novelty Search Optimization Method
Section 3: Advanced Neuroevolution Methods
Chapter 7: Hypercube-Based NEAT for Visual Discrimination
Chapter 8: ES-HyperNEAT and the Retina Problem
Chapter 9: Co-Evolution and the SAFE Method
Chapter 10: Deep Neuroevolution
Section 4: Discussion and Concluding Remarks
Chapter 11: Best Practices,Tips,and Tricks
Chapter 12: Concluding Remarks
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