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

OpenAI Gym

OpenAI Gym (https://github.com/openai/gym) is an open source library that was written to allow access to a standardized set of reinforcement learning tasks. It provides a toolkit that can be used to compare and develop reinforcement learning algorithms.

OpenAI Gym consists of a collection of environments, all presenting a common interface called env. This interface decouples the various environments from the agents, which can be implemented in any way we like—the only requirement from the agent is that it can interact with the environment(s) via the env interface. This will be described in the next subsection.

The basic package, gym, provides access to several environments and can be installed as follows:

pip install gym

Several other packages are available, such as 'Atari', 'Box2D', and 'MuJoCo', that provide access to numerous...