Book Image

Hands-On Genetic Algorithms with Python

By : Eyal Wirsansky
Book Image

Hands-On Genetic Algorithms with Python

By: Eyal Wirsansky

Overview of this book

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.
Table of Contents (18 chapters)
1
Section 1: The Basics of Genetic Algorithms
4
Section 2: Solving Problems with Genetic Algorithms
9
Section 3: Artificial Intelligence Applications of Genetic Algorithms
14
Section 4: Related Technologies

Solving the TSP

Imagine that you manage a small fulfillment center and need to deliver packages to a list of customers using a single vehicle. What is the best route for the vehicle to take so that you visit all your customers and then return to the starting point? This is an example of the classic TSP.

The TSP dates back to 1930, and since then has been and is one of the most thoroughly studied problems in optimization. It is often used to benchmark optimization algorithms. The problem has many variants, but it was originally based on a traveling salesman that needs to take a trip covering several cities:

"Given a list of cities and the distances between each pair of the cities, find the shortest possible path that goes through all the cities, and returns to the starting city."

Using combinatorics, you could find that when given n cities, the number of possible paths...