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Key benefits

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

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

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Amazon verified reviews

roudan Oct 11, 2020

Wow this is an awesome GA book which use DEAP to demonstrate how to solve easily various type of problems using GA. This is the best book ever in GA.It comes with complete codes which can used in my projects. I learnt a lot. Thank you!

Amazon Verified review

Arcueid Dec 08, 2020

仕事関係で購入して独学しています（QSAR分野でfeature selectionなど案外行けそうな感じがするので）。数学の内容を数式など使わずに平易な言語表現で語っており非常にやさしい本です。英語が問題なければ普通に一日1 chapter進められる。完読したら実務的にGAが使えると考えられます。追記：niching & sharingの解説と実装はこの本の肝要と思います。勉強になりました。必要な事前スキル・Pythonの基本作法・最低限の数学知識（高校生ないし学部教養レベル）・Machine learningの概念（ほんとうに概念だけ）

Mark on Amzon Mar 02, 2020

Starts with an overview of genetic algorithms (GA) then introduces the DEAP framework for evolutionary computation, which is used in the book for the various example problems/solutions. All of the code is explained and as you read about the examples and inspect the code, you can get ideas for possible applications of GA to real problems. Very gentle learning curve, but you should know some basic Python before starting this book.

A. Kashyap Mar 23, 2022

Easy to understand and extremely useful. Helped me out a great deal with a real work project.

Amazon Customer Jul 16, 2020

This was the best Genetic Algorithm book ever in my life. Due to its simplicity and pesudo-code-like nature of the Python language, the example codes does not interfere with the readers’ intellectual engagement into the beauty of evolutionary algorithms.

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

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