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

# A mean fitness versus generation graph

In the [previous chapter](https://www.educative.io/courses/genetic-algorithms-elixir/using-genetic-algorithms-to-simulate-evolution), one of the issues we encountered during your evolution was continuing the algorithm after it had already converged. When we analyzed the mean fitness at different generations, we saw that there was basically no change between the `500`th and `1000`th generation. This is because the algorithm had already converged.

The easiest way to recognize roughly when the algorithm converges is by creating a graph of mean fitness versus generation. When we do this, we’ll notice a dramatic plateau.


## Adding the `gnuplot-elixir` library

To create a basic graph, we’ll use [`gnuplot-elixir`](https://github.com/devstopfix/gnuplot-elixir) which is a port of the Gnuplot library. [Gnuplot](http://www.gnuplot.info) is a library for generating simple plots using the command line. We first need to ensure you have Gnuplot installed. In Ubuntu, we install Gnuplot like this:

```
$ sudo apt-get install gnuplot
```

Once it’s installed, we need to add `gnuplot-elixir` to your dependencies in `mix.exs`:

Learn how to create a mean fitness vs generation graph of tiger evolution framework.

Visualizing Basic Statistics

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

Visualizing Basic Statistics

A mean fitness versus generation graph