Gain insights into building genetic algorithm frameworks in Elixir. Learn about statistics, genealogy tracking, and solving practical problems with customizable genetic algorithm frameworks.

elixir.tar.gz

Default-elixir

Elixir-genetic

Elixir-genetic-v1

Elixir-genetic-v2

Elixir-breaker

Mix-command

Elixir-encode

Elixir-evaluate

Elixir-encode-speller

Elixir-evaluate-complete

Elixir-evaluate-v2

Elixir-evaluate-v3

Elixir-evaluate-portfolio

Elixir-selection

Elixir-generate

Elixir-generate-orderone

Elixir-selection-random

Elixir-selection-tournament

Elixir-selection-roulette

Elixir-generate-singlepoint

Elixir-generate-uniform

Elixir-generate-whole

Elixir-prematureconverge

Elixir-prematureconverge-complete

Elixir-prematureconverge-complete-nqueens

Elixir-replacing

Elixir-tiger

Elixir-tiger_nogene

Elixir-tiger_gene

Elixir-tiger_visualize-gui

Elixir-tiger_visualize-gui-two

Elixir-tetris

Elixir-benchee

Elixir-profiling

Elixir-pmap

Elixir-nifs

Elixir-testing

Elixir-testing-credo

Elixir-testing-dialyzer

Elixir-tiger_visualize-mtpaint

Elixir-tetris-copy

Elixir-agent-onemax

Elixir-testing-dialyzer-updated

Elixir-tiger_deps.get

Elixir-writing_deps

This course has been designed to introduce you to a field of programming you might have never been exposed to. In this course, you’ll learn everything you need to know to start working with genetic algorithms. As you work through the course, 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.

Genetic Algorithms in Elixir

# Applying our framework to the One-Max problem

With our framework built, it’s time to apply it to the One-Max problem. Firstly, open a terminal and create a new file in the scripts directory named `one_max.exs`. This has already been done.

Next, open the `one_max.exs` file. Now, think about what the framework already accomplishes and what parts of the problem need to be defined. What are the problem-specific parameters needed to pass into `run`?

Once the parameters are determined, we’ll start defining them. Logically, the first one is how we encode chromosomes. Remember, for the One-Max problem, we’re looking for the maximum sum of a bitstring of length `N`. To keep things consistent, the length should be `1000`. This means the solutions should be bitstrings of length `N`.

To define the genotype, add the following at the top of the `one_max.exs` file:

```
genotype = fn -> for _ <- 1..1000, do: Enum.random(0..1) end
```

This should look similar to what we did in the lesson [Introducing the One-Max Problem](https://www.educative.io/courses/genetic-algorithms-elixir/introducing-the-one-max-problem). All that’s missing is the outside for-loop.

However,  recall that the outside for-loop has been taken care of in the initialization step, so it’s unnecessary now.

The next step is to define our fitness function and termination criteria. How did we evaluate solutions in the previous chapter? Remember, we want a maximum sum, so fitness is just the sum. What’s the maximum possible sum we can achieve with a bitstring of length `1000`? Well, that would be `1000`! Therefore, the termination criteria or `max_fitness` is `1000`.

Below genotype, define your fitness function and termination criteria like this:

Learn how our defined framework can solve the One-Max problem along with seeing a brief overview of what to expect in the next chapter.

Solving the One-Max Problem Again

Introduction

Writing Your First Genetic Algorithm

Breaking Down Genetic Algorithms

Encoding Problems and Solutions

Evaluating Solutions and Populations

Selecting the Best

Generating New Solutions

Preventing Premature Convergence

Replacing and Transitioning

Tracking Genetic Algorithms

Visualizing the Results

Optimizing Your Algorithms

Writing Tests and Code Quality

Moving Forward

Solving the One-Max Problem Again

Applying our framework to the One-Max problem