From finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms.
Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind.
Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications.
Open your eyes to a unique and powerful field - without having to learn a new language or framework.
What You Need:
You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.
Author(s): Sean Moriarity
Edition: 1
Publisher: Pragmatic Bookshelf
Year: 2021
Language: English
Commentary: Vector PDF
Pages: 234
City: Raleigh, NC
Tags: Algorithms; Genetic Algorithms; Data Visualization; Elixir; Genomics; Testing
Cover
Table of Contents
Disclaimer
Acknowledgments
Preface
Who This Book Is For
What’s in This Book
How to Use This Book
How Does Elixir Fit In?
1. Writing Your First Genetic Algorithm
Understanding Genetic Algorithms
Introducing the One-Max Problem
Initializing the Population
Understanding the Flow of Genetic Algorithms
Selecting Parents
Creating Children
Running Your Solution
Adding Mutation
What You Learned
2. Breaking Down Genetic Algorithms
Reviewing Genetic Algorithms
Looking Deeper into Genetic Algorithms
Using Mix to Write Genetic Algorithms
Building a Framework for Genetic Algorithms
Understanding Hyperparameters
Solving the One-Max Problem Again
What You Learned
3. Encoding Problems and Solutions
Using Structs to Represent Chromosomes
Using Behaviours to Model Problems
Understanding and Choosing Genotypes
Solving One-Max for the Last Time
Spelling Words with Genetic Algorithms
What You Learned
4. Evaluating Solutions and Populations
Optimizing Cargo Loads
Introducing Penalty Functions
Applying a Penalty to the Shipping Problem
Defining Termination Criteria
Applying Termination Criteria to Shipping
Crafting Fitness Functions
Exploring Different Types of Optimization
What You Learned
5. Selecting the Best
Exploring Selection
Customizing Selection in Your Framework
Implementing Common Selection Strategies
What You Learned
6. Generating New Solutions
Introducing N-Queens
Solving N-Queens with Order-One Crossover
Exploring Crossover
Implementing Other Common Crossover Strategies
Crossing Over More Than Two Parents
Implementing Chromosome Repairment
What You Learned
7. Preventing Premature Convergence
Breaking Codes with Genetic Algorithms
Understanding Mutation
Customizing Mutation in Your Framework
Implementing Common Mutation Strategies
Other Methods to Combat Convergence
What You Learned
8. Replacing and Transitioning
Creating a Class Schedule
Understanding Reinsertion
Experimenting with Reinsertion
Growing and Shrinking Populations
Local Versus Global Reinsertion
What You Learned
9. Tracking Genetic Algorithms
Using Genetic Algorithms to Simulate Evolution
Logging Statistics Using ETS
Tracking Genealogy in a Genealogy Tree
What You Learned
10. Visualizing the Results
Visualizing the Genealogy of the Tiger Evolution
Visualizing Basic Statistics
Playing Tetris with Genetic Algorithms
Installing and Compiling ALEx
What You Learned
11. Optimizing Your Algorithms
Benchmarking and Profiling Genetic Algorithms
Writing Fast Elixir
Improving Performance with Parallelization
Improving Performance with NIFs
What You Learned
12. Writing Tests and Code Quality
Understanding Randomness
Writing Property Tests with ExUnit
Cleaning Up Your Framework
Writing Type Specifications
What You Learned
13. Moving Forward
Learning with Evolution
Designing with Evolution
Trading with Evolution
Networking with Evolution
Evolving Neural Networks
Where to Go Next
Bibliography
Index
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