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

Using the Toolbox class

The second mechanism offered by the DEAP framework is the base.Toolbox class. The Toolbox is used as a container for functions (or operators), and enables us to create new operators by aliasing and customizing existing functions.

For example, suppose we have a function, sumOfTwo(), defined as follows:

def sumOfTwo(a, b):
return a + b

Using toolbox, we can now create a new operator, incrementByFive(), which customizes the sumOfTwo() function as follows:

from deap import base
toolbox= base.Toolbox()
toolbox.register("incrementByFive", sumOfTwo, b=5)

The first argument passed to the register() toolbox function is the desired name (or alias) for the new operator. The second argument is the existing function to be customized. Then, each additional (optional) argument is automatically passed to the customized function whenever we call the new operator...