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Hands-On Genetic Algorithms with Python
Hands-On Genetic Algorithms with Python

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

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Hands-On Genetic Algorithms with Python

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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.

Who is this book for?

This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2020
Length: 346 pages
Edition : 1st
Language : English
ISBN-13 : 9781838559182
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Product Details

Publication date : Jan 31, 2020
Length: 346 pages
Edition : 1st
Language : English
ISBN-13 : 9781838559182
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Languages :
Tools :

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Frequently bought together


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Total $ 153.97
Mastering Machine Learning Algorithms
$48.99
Artificial Intelligence with Python
$54.99
Hands-On Genetic Algorithms with Python
$49.99
Total $ 153.97 Stars icon

Table of Contents

17 Chapters
Section 1: The Basics of Genetic Algorithms Chevron down icon Chevron up icon
An Introduction to Genetic Algorithms Chevron down icon Chevron up icon
Understanding the Key Components of Genetic Algorithms Chevron down icon Chevron up icon
Section 2: Solving Problems with Genetic Algorithms Chevron down icon Chevron up icon
Using the DEAP Framework Chevron down icon Chevron up icon
Combinatorial Optimization Chevron down icon Chevron up icon
Constraint Satisfaction Chevron down icon Chevron up icon
Optimizing Continuous Functions Chevron down icon Chevron up icon
Section 3: Artificial Intelligence Applications of Genetic Algorithms Chevron down icon Chevron up icon
Enhancing Machine Learning Models Using Feature Selection Chevron down icon Chevron up icon
Hyperparameter Tuning of Machine Learning Models Chevron down icon Chevron up icon
Architecture Optimization of Deep Learning Networks Chevron down icon Chevron up icon
Reinforcement Learning with Genetic Algorithms Chevron down icon Chevron up icon
Section 4: Related Technologies Chevron down icon Chevron up icon
Genetic Image Reconstruction Chevron down icon Chevron up icon
Other Evolutionary and Bio-Inspired Computation Techniques Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(12 Ratings)
5 star 91.7%
4 star 0%
3 star 8.3%
2 star 0%
1 star 0%
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roudan Oct 11, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Arcueid Dec 08, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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)
Amazon Verified review Amazon
Mark on Amzon Mar 02, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
A. Kashyap Mar 23, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Easy to understand and extremely useful. Helped me out a great deal with a real work project.
Amazon Verified review Amazon
Amazon Customer Jul 16, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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