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:
Hands-On Neuroevolution with Python
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Hands-On Neuroevolution with Python
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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.
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Current Title:
Hands-On Neuroevolution with Python
Table of Contents (18 chapters)
Preface
Free Chapter
Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods
Overview of Neuroevolution Methods
Python Libraries and Environment Setup
Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems
Using NEAT for XOR Solver Optimization
Pole-Balancing Experiments
Autonomous Maze Navigation
Novelty Search Optimization Method
Section 3: Advanced Neuroevolution Methods
Hypercube-Based NEAT for Visual Discrimination
ES-HyperNEAT and the Retina Problem
Co-Evolution and the SAFE Method
Deep Neuroevolution
Section 4: Discussion and Concluding Remarks
Best Practices, Tips, and Tricks
Concluding Remarks
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