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

Niching and sharing

In nature, any environment is further divided into multiple sub-environments, or niches, populated by various species taking advantage of the unique resources available in each niche, such as food and shelter. For example, a forest environment is comprised of the treetops, the shrubs, the forest floor, the tree roots, and so on; each of these accommodating different species who are specialized for living in that niche and takes advantage of its resources.

When several different species coexist in the same niche, they all compete over the same resources, and a tendency is created to search for new, unpopulated niches and populate them.

In the realm of genetic algorithms, this niching phenomenon can be used to maintain the diversity of the population as well as for finding several optimal solutions, each considered a niche.

For example, suppose our genetic algorithm...