Book Image

Hands-On Neuroevolution with Python

By : Iaroslav Omelianenko
Book Image

Hands-On Neuroevolution with Python

By: Iaroslav 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)
Free Chapter
1
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

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

The HyperNEAT method, which we discussed in Chapter 7, Hypercube-Based NEAT for Visual Discrimination, allows us to use neuroevolution methods for a broad class of problems that require the use of large-scale ANN structures to find a solution. This class of problem spreads across multiple practical domains, including visual pattern recognition. The main distinguishing feature of all these problems is the high dimensionality of the input/output data.

In the previous chapter, you learned how to define the configuration of the substrate of the discriminator ANN to solve a visual discrimination task. You also learned that it is crucial to use an appropriate substrate configuration that is aligned with the geometric features of the search space of the target problem. With the HyperNEAT method, you, as an...