Explore the evergrowing world of genetic algorithms to solve search optimization and AIrelated tasks and improve machine learning models using Python libraries such as DEAP scikitlearn and NumPy Key Features Explore the ins and outs of genetic algorithms with this fastpaced guide Implement tasks such as feature selection search optimization and cluster analysis using Python Solve combinatorial problems optimize functions and enhance the performance of artificial intelligence applications Book DescriptionGenetic algorithms are a family of search optimization and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide highquality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python covering the latest developments in artificial intelligence.After introducing you to genetic algorithms and their principles of operation youll understand how they differ from traditional algorithms and what types of problems they can solve. Youll then discover how they can be applied to search and optimization problems such as planning scheduling gaming and analytics. As you advance youll also learn how to use genetic algorithms to improve your machine learning and deep learning models solve reinforcement learning tasks and perform image reconstruction. Finally youll cover several related technologies that can open up new possibilities for future applications.By the end of this book youll have handson experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains. What You Will Learn Understand how to use stateoftheart Python tools to create genetic algorithmbased applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the