With conventional deep learning methods almost hitting a wall in terms of their capability, more and more researchers have started looking for alternative approaches to train artificial neural networks.
Deep machine learning is extremely effective for pattern recognition, but fails in tasks that require an understanding of context or previously unseen data. Many researchers, including Geoff Hinton, the father of the modern incarnation of deep machine learning, agree that the current approach to designing artificial intelligence systems is no longer able to cope with the challenges currently being faced.
In this book, we discuss a viable alternative to traditional deep machine learning methods—neuroevolution algorithms. Neuroevolution is a family of machine learning methods that use evolutionary algorithms to ease the solving of complex tasks such as games, robotics, and the simulation of natural processes. Neuroevolution algorithms are inspired by the process of natural selection. Very simple artificial neural networks can evolve to become very complex. The ultimate result of neuroevolution is the optimal topology of a network, which makes the model more energy-efficient and more convenient to analyze.
Throughout this book, you will learn about various neuroevolution algorithms and get practical skills in using them to solve different computer science problems—from classic reinforcement learning to building agents for autonomous navigation through a labyrinth. Also, you will learn how neuroevolution can be used to train deep neural networks to create an agent that can play classic Atari games.
This book aims to give you a solid understanding of neuroevolution methods by implementing various experiments using step-by-step guidance. It covers practical examples in areas such as games, robotics, and the simulation of natural processes, using real-world examples and datasets to help you better understand the concepts explored. After reading this book, you will have everything you need to apply neuroevolution methods to other tasks similar to the experiments presented.
In writing this book, my goal is to provide you with knowledge of cutting-edge technology that is a vital alternative to traditional deep learning. I hope that the application of neuroevolution algorithms in your projects will allow you to solve your currently intractable problems in an elegant and energy-efficient way.
