Table Of ContentEarly Praise for Genetic Algorithms in Elixir
A thorough coverage of genetic algorithms and Elixir from the ground up. Two
books for the price of one.
➤ Frances Buontempo
Director, Buontempo Consulting Ltd
A comprehensive guide to anyone interested in genetic algorithms and Elixir. Sean
does an amazing job at breaking down even the most complicated topics and
presenting them in a way that’s practical and easy to understand. His unique
approach to building genetic algorithms using Elixir is game-changing.
➤ Svilen Gospodinov
Freelance software engineer and consultant
Genetic Algorithms in Elixir is a fantastic resource for a programmer interested in
learning a growing new language and fascinating topic at the same time.
➤ Charlie Wasaff
Freelance software developer
We've left this page blank to
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Genetic Algorithms in Elixir
Solve Problems Using Evolution
Sean Moriarity
The Pragmatic Bookshelf
Raleigh, North Carolina
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Copyright © 2021 The Pragmatic Programmers, LLC.
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ISBN-13: 978-1-68050-794-2
Encoded using the finest acid-free high-entropy binary digits.
Book version: P1.0—January 2021
Contents
Disclaimer . . . . . . . . . . . . . ix
Acknowledgments . . . . . . . . . . . xi
Preface . . . . . . . . . . . . . . xiii
1. Writing Your First Genetic Algorithm . . . . . . . 1
Understanding Genetic Algorithms 2
Introducing the One-Max Problem 5
Initializing the Population 6
Understanding the Flow of Genetic Algorithms 7
Selecting Parents 9
Creating Children 10
Running Your Solution 11
Adding Mutation 13
What You Learned 14
2. Breaking Down Genetic Algorithms . . . . . . . 15
Reviewing Genetic Algorithms 15
Looking Deeper into Genetic Algorithms 16
Using Mix to Write Genetic Algorithms 22
Building a Framework for Genetic Algorithms 23
Understanding Hyperparameters 27
Solving the One-Max Problem Again 29
What You Learned 31
3. Encoding Problems and Solutions . . . . . . . 33
Using Structs to Represent Chromosomes 33
Using Behaviours to Model Problems 37
Understanding and Choosing Genotypes 43
Solving One-Max for the Last Time 46
Contents • vi
Spelling Words with Genetic Algorithms 47
What You Learned 50
4. Evaluating Solutions and Populations . . . . . . 51
Optimizing Cargo Loads 52
Introducing Penalty Functions 55
Applying a Penalty to the Shipping Problem 56
Defining Termination Criteria 57
Applying Termination Criteria to Shipping 61
Crafting Fitness Functions 62
Exploring Different Types of Optimization 65
What You Learned 69
5. Selecting the Best . . . . . . . . . . . 71
Exploring Selection 72
Customizing Selection in Your Framework 75
Implementing Common Selection Strategies 78
What You Learned 85
6. Generating New Solutions . . . . . . . . . 87
Introducing N-Queens 87
Solving N-Queens with Order-One Crossover 91
Exploring Crossover 95
Implementing Other Common Crossover Strategies 95
Crossing Over More Than Two Parents 102
Implementing Chromosome Repairment 103
What You Learned 105
7. Preventing Premature Convergence . . . . . . . 107
Breaking Codes with Genetic Algorithms 108
Understanding Mutation 113
Customizing Mutation in Your Framework 115
Implementing Common Mutation Strategies 117
Other Methods to Combat Convergence 122
What You Learned 123
8. Replacing and Transitioning . . . . . . . . 125
Creating a Class Schedule 126
Understanding Reinsertion 129
Experimenting with Reinsertion 133
Growing and Shrinking Populations 135
Contents • vii
Local Versus Global Reinsertion 136
What You Learned 137
9. Tracking Genetic Algorithms . . . . . . . . 139
Using Genetic Algorithms to Simulate Evolution 139
Logging Statistics Using ETS 144
Tracking Genealogy in a Genealogy Tree 150
What You Learned 155
10. Visualizing the Results . . . . . . . . . . 157
Visualizing the Genealogy of the Tiger Evolution 157
Visualizing Basic Statistics 160
Playing Tetris with Genetic Algorithms 162
Installing and Compiling ALEx 163
What You Learned 168
11. Optimizing Your Algorithms . . . . . . . . 169
Benchmarking and Profiling Genetic Algorithms 170
Writing Fast Elixir 175
Improving Performance with Parallelization 177
Improving Performance with NIFs 183
What You Learned 185
12. Writing Tests and Code Quality . . . . . . . . 187
Understanding Randomness 188
Writing Property Tests with ExUnit 189
Cleaning Up Your Framework 192
Writing Type Specifications 195
What You Learned 198
13. Moving Forward . . . . . . . . . . . 199
Learning with Evolution 199
Designing with Evolution 200
Trading with Evolution 201
Networking with Evolution 202
Evolving Neural Networks 203
Where to Go Next 204
Bibliography . . . . . . . . . . . . 205
Index . . . . . . . . . . . . . . 207
Disclaimer
This publication was privately produced and is not the product of an official
in the United States Army acting in an official capacity. The contents of this
publication, including words, images, and opinions, are unofficial and not to
be considered as the official views of the United States Military Academy,
United States Army, or Department of Defense. Neither this publication nor
its content are endorsed by the United States Military Academy, United States
Army, or Department of Defense.
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