Genetic Algorithms in Elixir: Solve Problems Using Evolution

Length: 244 pages
Edition: 1
Language: English
Publisher: Pragmatic Bookshelf
Publication Date: 2021-02-09
ISBN-10: 168050794X
ISBN-13: 9781680507942
Sales Rank: #1740934 (See Top 100 Books)

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 Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind.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.

Genetic Algorithms in Elixir
    About the Pragmatic Bookshelf
    Table of Contents
    Disclaimer
    Early Praise for Genetic Algorithms in Elixir
    Acknowledgments
    Preface
        Who This Book Is For
        What’s in This Book
        How to Use This Book
        How Does Elixir Fit In?
    Chapter 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
    Chapter 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
    Chapter 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
    Chapter 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
    Chapter 5: Selecting the Best
        Exploring Selection
        Customizing Selection in Your Framework
        Implementing Common Selection Strategies
        What You Learned
    Chapter 6: Generating New Solutions
        Introducing N-Queens
        Solving N-Queens with Order-One Crossover
        Exploring Crossover
        Implementing Other Common Crossover Strategie s
        Crossing Over More Than Two Parents
        Implementing Chromosome Repairment
        What You Learned
    Chapter 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
    Chapter 8: Replacing and Transitioning
        Creating a Class Schedule
        Understanding Reinsertion
        Experimenting with Reinsertion
        Growing and Shrinking Populations
        Local Versus Global Reinsertion
        What You Learned
    Chapter 9: Tracking Genetic Algorithms
        Using Genetic Algorithms to Simulate Evolutio n
        Logging Statistics Using ETS
        Tracking Genealogy in a Genealogy Tree
        What You Learned
    Chapter 10: Visualizing the Results
        Visualizing the Genealogy of the Tiger Evolut ion
        Visualizing Basic Statistics
        Playing Tetris with Genetic Algorithms
        Installing and Compiling ALEx
        What You Learned
    Chapter 11: Optimizing Your Algorithms
        Benchmarking and Profiling Genetic Algorithms
        Writing Fast Elixir
        Improving Performance with Parallelization
        Improving Performance with NIFs
        What You Learned
    Chapter 12: Writing Tests and Code Quality
        Understanding Randomness
        Writing Property Tests with ExUnit
        Cleaning Up Your Framework
        Writing Type Specifications
        What You Learned
    Chapter 13: Moving Forward
        Learning with Evolution
        Designing with Evolution
        Trading with Evolution
        Networking with Evolution
        Evolving Neural Networks
        Where to Go Next
    Bibliography
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