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

# Common selection strategies

Balancing genetic diversity with strong solutions can be difficult to achieve without a smart selection strategy. Fortunately, there are common selection strategies that are battle-tested and proven to work well for many different problem sets.

The selection strategies we’ll learn about in this course are the following:

* Elitism selection

* Random selection
* Tournament selection 
* Roulette selection

We’ll see how these strategies work, what each of their drawbacks are, and how to implement them in Elixir so we can add them to your toolbox.

## Elitism selection

**Elitism selection** is the simplest and most common selection strategy. The idea is simple: choose the best `n` chromosomes to reproduce. Elitism selection gives preference to the most elite chromosomes.

The problem with elitism selection is that it doesn’t factor genetic diversity into the mix at all. It’s common with elitism selection that your algorithms will converge onto a strong solution quickly but fail to improve from that point on because your population is too similar. Fortunately, we can counteract the lack of diversity with mutation, large populations, and even large chromosomes.

The algorithm behind elitism selection is straightforward. Given a population of sorted chromosomes, select the `n` best. The image shown below should help to visualize elitism selection:

Learn how different selection strategies can be implemented in Elixir.

Implementing Common Selection Strategies

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

Implementing Common Selection Strategies

Common selection strategies

Elitism selection