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

## Implementing the average tiger feature

Now that you have extensible statistics tracking in place, you can use it to monitor more insightful statistics for your evolution, such as the average tiger for each climate.

You’ve already identified the fittest tiger for each climate; however, what matters more is how the entire population changes in a given climate. To identify this, you can implement an `average_tiger` statistic that tells you the average tiger for any given generation.

To do this, start by creating the following `average_tiger/1` function in your `TigerSimulation` module:

# Implementing the average tiger feature

Now that you have extensible statistics tracking in place, you can use it to monitor more insightful statistics for your evolution, such as the average tiger for each climate.

You’ve already identified the fittest tiger for each climate; however, what matters more is how the entire population changes in a given climate. To identify this, you can implement an `average_tiger` statistic that tells you the average tiger for any given generation.

To do this, start by creating the following `average_tiger/1` function in your `TigerSimulation` module:

Learn how to monitor and add additional statistics in different climates to the framework.

Finding the Average Tiger

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

Finding the Average Tiger

Implementing the average tiger feature