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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

Where to go from here

We hope that your journey through the neuroevolution methods that were presented in this book was pleasant and insightful. We have done our best to present you with the most recent achievements in the field of neuroevolution. However, this field of applied computer science is developing rapidly, and new achievements are announced almost every month. There are many laboratories in universities, as well as in corporations around the globe, working on applying neuroevolution methods to solve tasks that are beyond the strength of mainstream deep learning algorithms.

We hope that you have become fond of the neuroevolution methods we discussed and are eager to apply them in your work and experiments. However, you need to continue your self-education to keep pace with the next achievements in the area. In this section, we will present some places where you can...

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