Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP and XAI, using Python libraries such as DEAP, scikit-learn, and NumPy. Key Features Learn to implement genetic algorithms using -of-the-art Python tools Take advantage of cloud computing technology to increase the performance of your solutions Discover related bio-inspired algorithms such as particle swarm optimization and NEAT Book DescriptionGenetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. This book will guide you through mastering the powerful, yet simple technique of genetic algorithms, applying them to a wide range of tasks and using them in artificial intelligence applications, all with Python. After introducing genetic algorithms and their principles of operation, you will find out how they differ from traditional algorithms and the types of problems they can solve. Discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. Learn to apply genetic algorithms to artificial intelligence - to improve machine learning and deep learning models, solve reinforcement learning as well as natural language processing tasks, and delve into explainable AI. The new edition expands on applying genetic algorithms to NLP and XAI, speeding up genetic algorithms with concurrency and cloud computing, and understanding the NEAT algorithm. The book concludes with an image reconstruction project, followed by demonstrations of related technologies, for future applications. By the end of this book, you will have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence.What you will learn Use genetic algorithms to solve planning, scheduling, gaming, and analytics problems Create reinforcement learning, natural language processing, and explainable AI applications Enhance the performance of machine learning models and optimize deep learning architecture Increase the performance of your solutions by taking advantage of concurrency and cloud computing Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as particle swarm optimization and NEAT Who this book is forIf you are a data scientist, software developer, AI enthusiast who wants to break into genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. To learn from this book, you should have a working knowledge of Python.

Book Information
ISBN 9781805123798
Author Eyal Wirsansky
Format Paperback
Page Count 451
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited