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

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.

Table of Contents

An Introduction to Genetic Algorithms

Understanding the Key Components of Genetic Algorithms

Using the DEAP Framework

Combinatorial Optimization

Constraint Satisfaction

Optimizing Continuous Functions

Enhancing Machine Learning Models Using Feature Selection

Hyperparameter Tuning Machine Learning Models

Architecture Optimization of Deep Learning Networks

Reinforcement Learning with Genetic Algorithms

Genetic Image Reconstruction