Preface

Summary

Sean Moriarity's book "Genetic Algorithms in Elixir" introduces Elixir programmers to genetic algorithms, providing a comprehensive guide to applying these natural problem-solving strategies to various fields through a practical framework.

Abstract

"Genetic Algorithms in Elixir" by Sean Moriarity is a resource aimed at Elixir developers interested in the field of genetic algorithms, a computational problem-solving method inspired by natural evolution. The book addresses the scarcity of educational materials on genetic algorithms, particularly for Elixir programmers, and aims to demystify the subject. It promises to equip readers with the knowledge to build a full-featured genetic algorithm framework in Elixir, covering topics such as statistics and genealogy tracking, while also offering insights into idiomatic Elixir coding practices for problem-solving and algorithm verification. By the end of the book, readers should be able to apply genetic algorithms to practical problems within fields like finance, logistics, and artificial intelligence without needing to learn a new programming language.

Opinions

The author believes that genetic algorithms are an underutilized tool in programming despite their effectiveness in solving complex problems.
There is a perceived lack of resources for programmers to learn about genetic algorithms, especially tailored for Elixir developers.
The book is intended to lower the barrier to entry for Elixir programmers to explore genetic algorithms without the need to master a new language for this purpose.
The author expresses enthusiasm for the beauty and potential of genetic algorithms to inspire innovative thinking and problem-solving in various domains.
The book emphasizes practical application and includes a comprehensive framework for implementing genetic algorithms, with a focus on Elixir-specific coding techniques and performance optimization.

Genetic Algorithms in Elixir — by Sean Moriarity (4 / 101)

Genetic algorithms are a powerful and often overlooked tool for solving difficult problems. Some of the most beautiful solutions to practical problems are inspired by or modeled after solutions found in mother nature. Genetic algorithms are no exception. Inspired by the original optimization algorithm — evolution — genetic algorithms can be used to solve a variety of problems in a variety of fields. As you’ll see in this book, genetic algorithms have applications in finance, logistics, artificial intelligence, and more.

Unfortunately, despite being one of the first “artificial intelligence” algorithms, there’s a surprising lack of resources available for programmers to explore the ins and outs of using evolution to solve problems. Even still, there are no books designed specifically with Elixir programmers in mind.

My goal in writing this book is to introduce Elixir programmers to a field of programming they might have never been exposed to or were too intimidated to try. Technology evolves rapidly, and programmers need to constantly seek out and learn about new fields and new technologies. While Elixir may not be the ideal language for solving computationally expensive problems, a programmer shouldn’t be forced to learn a new language just to learn about genetic algorithms.

In this book, you’ll learn everything you need to know to start working with genetic algorithms. As you work through the book, you’ll build a framework for problems using genetic algorithms. By the end, you’ll have a full-featured, customizable framework complete with statistics, genealogy tracking, and more, and you’ll have learned everything you need to solve practical problems with genetic algorithms. You’ll do all of this using Elixir. Along the way, you’ll learn some Elixir-specific tips and tricks to idiomatically encode problems and solutions, speed up your code, and verify the correctness of the algorithms you implement.

I hope this book forces you to think outside the box and inspires you to further explore the beauty of genetic algorithms.

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