Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

by Eyal Wirsansky

Overview

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

Key Features

• Explore the ins and outs of genetic algorithms with this fast-paced 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 Description

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 of applying genetic algorithms in artificial intelligence as well as in numerous other domains.

What you will learn

• Understand how 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

Who this book is for

This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.

Product Details

ISBN-13:	9781838559182
Publisher:	Packt Publishing
Publication date:	01/31/2020
Sold by:	Barnes & Noble
Format:	eBook
Pages:	346
File size:	9 MB

About the Author

Eyal Wirsansky is a senior data scientist, an experienced software engineer, a technology community leader, and an artificial intelligence researcher. Eyal began his software engineering career over twenty-five years ago as a pioneer in the field of Voice over IP. He currently works as a member of the data platform team at Gradle, Inc. During his graduate studies, he focused his research on genetic algorithms and neural networks. A notable result of this research is a novel supervised machine learning algorithm that integrates both approaches. In addition to his professional roles, Eyal serves as an adjunct professor at Jacksonville University, where he teaches a class on artificial intelligence. He also leads both the Jacksonville, Florida Java User Group and the Artificial Intelligence for Enterprise virtual user group, and authors the developer-focused artificial intelligence blog, ai4java.

Table of Contents

Table of Contents

1. An Introduction to Genetic Algorithms
2. Understanding the Key Components of Genetic Algorithms
3. Using the DEAP Framework
4. Combinatorial Optimization
5. Constraint Satisfaction
6. Optimizing Continuous Functions
7. Enhancing Machine Learning Models Using Feature Selection
8. Hyperparameter Tuning Machine Learning Models
9. Architecture Optimization of Deep Learning Networks
10. Reinforcement Learning with Genetic Algorithms
11. Genetic Image Reconstruction
12. Other Evolutionary and Bio-Inspired Computation Techniques