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Genetic Algorithms in Elixir

Genetic Algorithms in Elixir

Gain insights into building genetic algorithm frameworks in Elixir. Learn about statistics, genealogy tracking, and solving practical problems with customizable genetic algorithm frameworks.

Beginner

74 Lessons

25h 30min

Certificate of Completion

Gain insights into building genetic algorithm frameworks in Elixir. Learn about statistics, genealogy tracking, and solving practical problems with customizable genetic algorithm frameworks.

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This course includes

52 Playgrounds
16 Quizzes

This course includes

52 Playgrounds
16 Quizzes
Course Overview
What You'll Learn
Course Content

Course Overview

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 a...Show More

What You'll Learn

Learn about the basics of Genetic Algorithms in Elixir.

Learn how to design the framework for using Genetic Algorithms.

Explore the processes of selection, crossover, mutation and reinsertion.

Analyze the performance of the Genetic Algorithms by benching and profiling them.

Explore the different ways of visualizing Genetic Algorithms along with testing and type checking your code.

What You'll Learn

Learn about the basics of Genetic Algorithms in Elixir.

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Course Content

1.

Introduction

Get familiar with Genetic Algorithms in Elixir, their applications, and Elixir's advantages.
A Guide to Follow This Course
What to expect from this course?
Why Elixir?
2.

Writing Your First Genetic Algorithm

Look at using Elixir to build genetic algorithms for optimization and problem-solving.
Understanding Genetic Algorithms
Role of Exploration and Exploitation in Genetic Algorithms
Introducing the One-Max Problem
Understanding the Flow of Genetic Algorithms
The Evaluation, Selection and Crossover Steps
Adding Mutation to our Genetic Algorithm
3.

Breaking Down Genetic Algorithms

Work your way through the fundamental structure and processes of genetic algorithms in Elixir.
Looking Deeper into Genetic Algorithms
Selection, Crossover, Mutation and Termination
Building a Framework for Genetic Algorithms
Adding the Missing Pieces to Functions
Understanding Hyperparameters
Solving the One-Max Problem Again
4.

Encoding Problems and Solutions

Grasp the fundamentals of enhancing genetic algorithms with Elixir's structs and behaviours.
Understanding and Creating Chromosomes
Using Behaviours to Model Problems
Creating the Problem Behaviour
Understanding and Choosing Genotypes
Using the New Framework in One-Max problem
Using the New Framework in spelling word problem
5.

Evaluating Solutions and Populations

Take a closer look at optimizing cargo loads, penalty functions, termination criteria, and crafting fitness functions.
Optimizing Cargo Loads
Introducing Penalty Functions
Defining Termination Criteria
Applying Termination Criteria to the Cargo Problem
Crafting Fitness Functions
Exploring Different Types of Optimization
6.

Selecting the Best

5 Lessons

Incorporate various selection strategies in genetic algorithms to balance diversity and fitness.
7.

Generating New Solutions

7 Lessons

Piece together the parts of effective crossover strategies to enhance genetic algorithm solutions.
8.

Preventing Premature Convergence

5 Lessons

Break down methods to prevent premature convergence using mutation strategies and diverse techniques.
9.

Replacing and Transitioning

5 Lessons

Unpack the core of genetic algorithms for class scheduling, reinsertion, and population management strategies.
10.

Tracking Genetic Algorithms

6 Lessons

Examine genetic algorithm simulations and track evolution statistics, genealogy trees, and adaptive traits.
11.

Visualizing the Results

4 Lessons

Apply your skills to visualizing genetic algorithm results and creating AI agents for Atari games.
12.

Optimizing Your Algorithms

7 Lessons

Take a closer look at optimizing genetic algorithms through benchmarking, profiling, and parallelization in Elixir.
13.

Writing Tests and Code Quality

4 Lessons

Tackle code testing, property tests, code cleanup, and type specifications for reliable Elixir frameworks.
14.

Moving Forward

4 Lessons

Build on the role of genetic algorithms in AI, real-life applications, neural networks, and advanced exploration.

Course Author

Practice and apply your skills

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Anthony Walker

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Evan Dunbar

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Carlos Matias La Borde

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Souvik Kundu

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Eric Downs

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Kenan Eyvazov

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Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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