Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems
Authors: Eyal Wirsansky
ISBN-10: 1838557741
ISBN-13: 9781838557744
Released: 2020-01-31
Print Length 页数: 346 pages
Publisher finelybook 出版社: Packt
Book Description
Explore the ever-growing world of genetic algorithms to solve search,optimization,and AI-related tasks,and improve machine learning models using Python libraries such as DEAP,scikit-learn,and NumPy
Genetic 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 high-quality 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,you’ll understand how they differ from traditional algorithms and what types of problems they can solve. You’ll then discover how they can be applied to search and optimization problems,such as planning,scheduling,gaming,and analytics. As you advance,you’ll 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,you’ll cover several related technologies that can open up new possibilities for future applications.
By the end of this book,you’ll have hands-on experience applying genetic algorithms in artificial intelligence as well as numerous other domains.
What you will learn
Learn to use state-of-the-art Python tools to create genetic algorithm-based applications
Use genetic algorithms to optimize functions and solve planning and scheduling problems
Enhance the performance of machine learning models and optimize deep-learning network architecture
Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym
Explore how images can be reconstructed using a set of semi-transparent shapes
Discover other bio-inspired techniques such as genetic programming and particle swarm optimization
Contents
Preface
Section 1: The Basics of Genetic Algorithms
Chapter 1: An Introduction to Genetic Algorithms
Chapter 2: Understanding the Key Components of Genetic Algorithms
Section 2: Solving Problems with Genetic Algorithms
Chapter 3: Using the DEAP Framework
Chapter 4: Combinatorial Optimization
Chapter 5: Constraint Satisfaction
Chapter 6: Optimizing Continuous Functions
Section 3: Artificial Inteligence Applications of Genetic Algorithms
Chapter 7: Enhancing Machine Learning Models Using Feature Selection
Chapter 8: Hyperparameter Tuning of Machine Learning Models
Chapter 9: Architecture Optimization of Deep Learning Networks
Chapter 10: Reinforcement Learning with Genetic Algorithms
Section 4: Related Technologies
Chapter 11: Genetic Image Reconstruction
Chapter 12: Other Evolutionary and Bio-Inspired Computation Techniques
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