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

## What is uniform crossover?

Uniform crossover is a slightly more advanced crossover strategy in which genes in the parent chromosome are treated separately. While single-point crossover only works on blocks of genes, uniform crossover works on individual genes.

Uniform crossover works by pairing corresponding genes in a chromosome and swapping them according to a rate. A rate is a number between _0_ and _1_ that represents the probability that two genes will be swapped. A rate of _0.5_ indicates that genes have a 50% chance of swapping between parent chromosomes.

The image given below demonstrates uniform crossover:

# What is uniform crossover?

Uniform crossover is a slightly more advanced crossover strategy in which genes in the parent chromosome are treated separately. While single-point crossover only works on blocks of genes, uniform crossover works on individual genes.

Uniform crossover works by pairing corresponding genes in a chromosome and swapping them according to a rate. A rate is a number between _0_ and _1_ that represents the probability that two genes will be swapped. A rate of _0.5_ indicates that genes have a 50% chance of swapping between parent chromosomes.

The image given below demonstrates uniform crossover:

Learn about the implementation of uniform crossover.

Uniform Crossover

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

Uniform Crossover

What is uniform crossover?