Genetic Algorithms in Elixir

Table of contents

1.  Disclaimer
2.  Acknowledgments
3.  Preface
   1. Who This Book Is For
   2. What’s in This Book
   3. How to Use This Book
   4. How Does Elixir Fit In?
4. 1. Writing Your First Genetic Algorithm
   1. Understanding Genetic Algorithms
   2. Introducing the One-Max Problem
   3. Initializing the Population
   4. Understanding the Flow of Genetic Algorithms
   5. Selecting Parents
   6. Creating Children
   7. Running Your Solution
   8. Adding Mutation
   9. What You Learned
5. 2. Breaking Down Genetic Algorithms
   1. Reviewing Genetic Algorithms
   2. Looking Deeper into Genetic Algorithms
   3. Using Mix to Write Genetic Algorithms
   4. Building a Framework for Genetic Algorithms
   5. Understanding Hyperparameters
   6. Solving the One-Max Problem Again
   7. What You Learned
6. 3. Encoding Problems and Solutions
   1. Using Structs to Represent Chromosomes
   2. Using Behaviours to Model Problems
   3. Understanding and Choosing Genotypes
   4. Solving One-Max for the Last Time
   5. Spelling Words with Genetic Algorithms
   6. What You Learned
7. 4. Evaluating Solutions and Populations
   1. Optimizing Cargo Loads
   2. Introducing Penalty Functions
   3. Applying a Penalty to the Shipping Problem
   4. Defining Termination Criteria
   5. Applying Termination Criteria to Shipping
   6. Crafting Fitness Functions
   7. Exploring Different Types of Optimization
   8. What You Learned
8. 5. Selecting the Best
   1. Exploring Selection
   2. Customizing Selection in Your Framework
   3. Implementing Common Selection Strategies
   4. What You Learned
9. 6. Generating New Solutions
   1. Introducing N-Queens
   2. Solving N-Queens with Order-One Crossover
   3. Exploring Crossover
   4. Implementing Other Common Crossover Strategies
   5. Crossing Over More Than Two Parents
   6. Implementing Chromosome Repairment
   7. What You Learned
10. 7. Preventing Premature Convergence
    1. Breaking Codes with Genetic Algorithms
    2. Understanding Mutation
    3. Customizing Mutation in Your Framework
    4. Implementing Common Mutation Strategies
    5. Other Methods to Combat Convergence
    6. What You Learned
11. 8. Replacing and Transitioning
    1. Creating a Class Schedule
    2. Understanding Reinsertion
    3. Experimenting with Reinsertion
    4. Growing and Shrinking Populations
    5. Local Versus Global Reinsertion
    6. What You Learned
12. 9. Tracking Genetic Algorithms
    1. Using Genetic Algorithms to Simulate Evolution
    2. Logging Statistics Using ETS
    3. Tracking Genealogy in a Genealogy Tree
    4. What You Learned
13. 10. Visualizing the Results
    1. Visualizing the Genealogy of the Tiger Evolution
    2. Visualizing Basic Statistics
    3. Playing Tetris with Genetic Algorithms
    4. Installing and Compiling ALEx
    5. What You Learned
14. 11. Optimizing Your Algorithms
    1. Benchmarking and Profiling Genetic Algorithms
    2. Writing Fast Elixir
    3. Improving Performance with Parallelization
    4. Improving Performance with NIFs
    5. What You Learned
15. 12. Writing Tests and Code Quality
    1. Understanding Randomness
    2. Writing Property Tests with ExUnit
    3. Cleaning Up Your Framework
    4. Writing Type Specifications
    5. What You Learned
16. 13. Moving Forward
    1. Learning with Evolution
    2. Designing with Evolution
    3. Trading with Evolution
    4. Networking with Evolution
    5. Evolving Neural Networks
    6. Where to Go Next
17.  Bibliography