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

# Filling in the blanks

> Remember that we left blanks in the `evolve` and `run` functions, and that none of the functions we called in `evolve` took parameters? It’s time to fill in these blanks.

The functions `initialize/2` and `evaluate/2` take extra parameters aside from a `population`. Because we won’t be calling these functions individually outside the module, we can take these parameters inside the `run` and `evolve` functions and pass them to `initialize` and `evaluate` from there. 

We need to take the following parameters into both `run` and `evaluate`: `fitness_function`, `genotype`, and `max_fitness`. Additionally, with the required parameters in place, we can fill in the blanks left in the outline of the `run` and `evolve` functions.

The new modified versions of these functions look like the following:

Get to know about the missing pieces about the evolve and run functions. 

Adding the Missing Pieces to Functions

Get to know about the missing pieces about the evolve and run functions.

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

Adding the Missing Pieces to Functions

Filling in the blanks