Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms

Increase the performance of various neural network architectures using NEAT HyperNEAT ES-HyperNEAT Novelty Search SAFE and deep neuroevolutionKey FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games robotics and simulationsBook DescriptionNeuroevolution 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.Youll start with learning the key neuroevolution concepts and methods by writing code with Python. Youll 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 youll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later youll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally youll 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.What you will learnDiscover the most popular neuroevolution algorithms – NEAT HyperNEAT and ES-HyperNEATExplore how to implement neuroevolution-based algorithms in PythonGet up to speed with advanced visualization tools to examine evolved neural network graphsUnderstand how to examine the results of experiments and analyze algorithm performanceDelve into neuroevolution techniques to improve the performance of existing methodsApply deep neuroevolution to develop agents for playing Atari gamesWho this book is forThis book is for machine learning practitioners deep learning researchers and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.