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 abstraction?

Recall that one technique to solving problems is to transform them into a form we already understand. While every problem seems different and may require different techniques to solve, they almost always have patterns and similarities between them. This is especially true with the problems we’ll solve with genetic algorithms.

The framework you built in the lesson [Building a Framework for Genetic Algorithms](https://www.educative.io/courses/genetic-algorithms-elixir/building-a-framework-for-genetic-algorithms) separates a few problem-specific parameters from the common aspects of a genetic algorithm. These parameters are a fitness function, a genotype, and termination criteria. This means that every problem we attempt to solve using a genetic algorithm must implement all three of these functions. This therefore creates an unavoidable **natural abstraction** for problems.

An abstraction is a simplification of underlying complexities and implementations. The purpose of abstraction is to force us to think of things at different levels of specificity. It gives us an idea of what to look for before we approach a problem.

This is especially useful when approaching new problems with genetic algorithms. When we want to approach a new problem, we already know that we need a fitness function or a way to measure success, a genotype or a way to represent solutions, and some termination criteria, or a way to tell the algorithm when to stop. While the specifics are the difficult part, we’re never starting from scratch with this abstraction in place.

# What is abstraction?

Recall that one technique to solving problems is to transform them into a form we already understand. While every problem seems different and may require different techniques to solve, they almost always have patterns and similarities between them. This is especially true with the problems we’ll solve with genetic algorithms.

The framework you built in the lesson [Building a Framework for Genetic Algorithms](https://www.educative.io/courses/genetic-algorithms-elixir/building-a-framework-for-genetic-algorithms) separates a few problem-specific parameters from the common aspects of a genetic algorithm. These parameters are a fitness function, a genotype, and termination criteria. This means that every problem we attempt to solve using a genetic algorithm must implement all three of these functions. This therefore creates an unavoidable **natural abstraction** for problems.

An abstraction is a simplification of underlying complexities and implementations. The purpose of abstraction is to force us to think of things at different levels of specificity. It gives us an idea of what to look for before we approach a problem.

This is especially useful when approaching new problems with genetic algorithms. When we want to approach a new problem, we already know that we need a fitness function or a way to measure success, a genotype or a way to represent solutions, and some termination criteria, or a way to tell the algorithm when to stop. While the specifics are the difficult part, we’re never starting from scratch with this abstraction in place.

Learn about the concept of abstraction as well as behaviours. 

Using Behaviours to Model Problems

Learn about the concept of abstraction as well as behaviours.

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

Solving the 8 Queens Problem Using a Genetic Algorithm in Elixir

Moving Forward

Using Behaviours to Model Problems

What is abstraction?