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

# Recap

Throughout this course, you learned how to use genetic algorithms to solve optimization problems. You discovered the ins and outs of basic genetic algorithms and worked through solving difficult problems with a problem-solving framework. You designed a genetic algorithm framework from start to finish and expanded on this framework with the addition of basic tracking mechanisms and visualizations. You then optimized your framework with tools like ExProf and Benchee, and you learned how to ensure the code you implement within your framework is correct using tools like ExUnit and dialyxir.

At this point, you have a powerful suite of skills and tools that can be built upon or used to solve practically any problem with a genetic algorithm. One thing you might be wondering now is, “Where do I go next?”

In this chapter, you’ll learn about some of the recent advancements in genetic algorithms, and the recent innovations driven by genetic algorithms and evolutionary algorithms. Additionally, you’ll become familiar with some advanced resources that you can continue to use on your journey with genetic algorithms.


# Learning with evolution

**Artificial intelligence (AI)** and **machine learning (ML)** have dominated the last decade of computing. AI is experiencing its third “boom” with no signs of stopping. It seems that almost every day there’s a new revolutionary innovation in AI or another startup using AI to improve everyday life. The demand for developers who are familiar with and can solve problems using AI/ML will continue to increase over the next decade.

Learn about some of the recent innovations performed by genetic algorithms in the field of AI. 

Genetic Algorithms Role in AI 

Learn about some of the recent innovations performed by genetic algorithms in the field of AI.

Genetic Algorithms Role in AI

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

Genetic Algorithms Role in AI

Recap