Ivan Gridin - Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities of a powerful search algorithm (English Edition)

www.bpbonline.com

FIRST EDITION 2021

Copyright BPB Publications, India

ISBN: 978-81-94837-756

All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means.

LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY

The information contained in this book is true to correct and the best of authors and publishers knowledge. The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book.

All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information.

Distributors:

BPB PUBLICATIONS

20, Ansari Road, Darya Ganj

New Delhi-110002

Ph: 23254990/23254991

MICRO MEDIA

Shop No. 5, Mahendra Chambers,

150 DN Rd. Next to Capital Cinema,

V.T. (C.S.T.) Station, MUMBAI-400 001

Ph: 22078296/22078297

DECCAN AGENCIES

4-3-329, Bank Street,

Hyderabad-500195

Ph: 24756967/24756400

BPB BOOK CENTRE

376 Old Lajpat Rai Market,

Delhi-110006

Ph: 23861747

Published by Manish Jain for BPB Publications, 20 Ansari Road, Darya Ganj, New Delhi-110002 and Printed by him at Repro India Ltd, Mumbai

www.bpbonline.com

My lovely little daughters: Ksenia and Elena.
Your endless love and energy charge me every day.

And to my beautiful and patient wife Tamara.
Thats all thanks to you. You are the light of my life.

I love you

Ivan Gridin is a Mathematician, Fullstack Developer, Data Scientist, and Machine Learning Expert living in Moscow, Russia. Over the years, he worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the key areas of his research is the design and analysis of predictive time series models.

Ivan has fundamental math skills in probability theory, random process theory, time series analysis, machine learning, deep learning, and optimization. He also has in-depth knowledge and understanding of various programming languages such as Java, Python, PHP, and MATLAB.

Loving father, husband, and collector of old math books.

Linkedin: http://www.linkedin.com/in/survex/

Satyajeet Dhawale is a professional Data Scientist having a strong experience in machine learning, deep learning, computer vision, inferential and descriptive statistical analysis. He has worked on many projects that involve complex machine learning and deep learning algorithms and used a variety of data sets from a different domain. In his career, he has successfully delivered many machine learning and deep learning solutions for complex data problems. You can find more professional details about Satyajeet on LinkedIn. (https://www.linkedin.com/in/satyajeet-dhawale/)

There are a few people I want to thank for the idea and the motivation for writing this book. I thank my adorable wife Tamara; her patience and beauty inspired me every day. I thank my elder daughter Ksenia; her courage and determination motivated me in exhaustion moments. And my little daughter Elena for waking me up earlier youre my energizer!

Thanks to my mom for her love and the genetics she gave me.

I am eternally grateful to my company AT Consulting. This company has done a lot to make me the specialist I am. And I especially want to thank Alexey Korotaev - one of the best managers Ive ever seen. Im proud to be a part of this big team.

Thanks to my friends, who helped me in all my endeavors. To Petr Rostov for his help in learning mathematics and programming. To my friend Yuri Babaev for his inspiration and humor that he brings into my life. To Denis Paramanov for sacrificing his vacation for my future wife and me. To Dmitry Suvorov for his effective gym workout. To Sergey Korsikov and Alena Gulyaeva for helping us to get used to this big city. This book would be impossible without all of them.

I want to give the warmest hugs to my new big family in Perm and Khabarovsk. I especially want to hug Konstantin Volosatov, the most handsome and intelligent policeman I have ever met, and his beautiful wife, Elena Volosatova. We love you all very much!

My gratitude also goes to the book reviewer Satyajeet Dhawale. His participation and useful advice have made this book much better.

Special thanks to BPB Publications for support, advice, and assistance in creating and publishing this book.

There is a lot of talk about Machine Learning, Deep Learning, Neural Networks, and many other terms and technologies that provides Artificial Intelligence in our life. These technologies are used everywhere in our daily life. Image and terrain recognition techniques are being used in autonomous vehicles that are already driving worldwide. And the number of autonomous cars is increasing at a tremendous rate.

Artificial intelligence frameworks and libraries are beginning to penetrate very deeply into all areas of programming. An ordinary programmer needs to have at least a basic understanding of what machine learning is, what kind of tasks it solves, and how to work with it. Machine learning contains a broad set of tools. Most of the books and teaching materials describe only a few of them, while the other approaches based on the evolutionary search for solving problems remain uncovered.

This book is about Genetic Algorithms. It tells how the principles formulated by Charles Darwin in his book The Origin of Species in 1859 in our time help to solve the most complex problems that contain billions and billions of solutions. Genetic Algorithms solve problems that cannot be solved analytically. It mimics the process of evolution and natural selection in a population, where each individual represents a solution to a problem. The more vital individuals from the population outlive the weaker ones and produce the next generation of individuals. So after the evolution process, after many generations, we get some solution to the problem, which will highly likely satisfy the requirements.