AI-powered learning
Save this course
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.
74 Lessons
25h 30min
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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.
Learning Roadmap
1.
Introduction
Introduction
Get familiar with Genetic Algorithms in Elixir, their applications, and Elixir's advantages.
2.
Writing Your First Genetic Algorithm
Writing Your First Genetic Algorithm
Look at using Elixir to build genetic algorithms for optimization and problem-solving.
3.
Breaking Down Genetic Algorithms
Breaking Down Genetic Algorithms
6 Lessons
6 Lessons
Work your way through the fundamental structure and processes of genetic algorithms in Elixir.
4.
Encoding Problems and Solutions
Encoding Problems and Solutions
6 Lessons
6 Lessons
Grasp the fundamentals of enhancing genetic algorithms with Elixir's structs and behaviours.
5.
Evaluating Solutions and Populations
Evaluating Solutions and Populations
6 Lessons
6 Lessons
Take a closer look at optimizing cargo loads, penalty functions, termination criteria, and crafting fitness functions.
6.
Selecting the Best
Selecting the Best
5 Lessons
5 Lessons
Incorporate various selection strategies in genetic algorithms to balance diversity and fitness.
7.
Generating New Solutions
Generating New Solutions
7 Lessons
7 Lessons
Piece together the parts of effective crossover strategies to enhance genetic algorithm solutions.
8.
Preventing Premature Convergence
Preventing Premature Convergence
5 Lessons
5 Lessons
Break down methods to prevent premature convergence using mutation strategies and diverse techniques.
9.
Replacing and Transitioning
Replacing and Transitioning
5 Lessons
5 Lessons
Unpack the core of genetic algorithms for class scheduling, reinsertion, and population management strategies.
10.
Tracking Genetic Algorithms
Tracking Genetic Algorithms
6 Lessons
6 Lessons
Examine genetic algorithm simulations and track evolution statistics, genealogy trees, and adaptive traits.
11.
Visualizing the Results
Visualizing the Results
4 Lessons
4 Lessons
Apply your skills to visualizing genetic algorithm results and creating AI agents for Atari games.
12.
Optimizing Your Algorithms
Optimizing Your Algorithms
7 Lessons
7 Lessons
Take a closer look at optimizing genetic algorithms through benchmarking, profiling, and parallelization in Elixir.
13.
Writing Tests and Code Quality
Writing Tests and Code Quality
4 Lessons
4 Lessons
Tackle code testing, property tests, code cleanup, and type specifications for reliable Elixir frameworks.
14.
Moving Forward
Moving Forward
4 Lessons
4 Lessons
Build on the role of genetic algorithms in AI, real-life applications, neural networks, and advanced exploration.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
The Pragmatic Programmers
We create timely, practical books and learning resources on classic and cutting-edge topics to help you practice your craft and accelerate your career.
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
Built for 10x Developers
No Passive Learning
Learn by building with project-based lessons and in-browser code editor
Personalized Roadmaps
The platform adapts to your strengths & skills gaps as you go
Future-proof Your Career
Get hands-on with in-demand skills
AI Code Mentor
Write better code with AI feedback, smart debugging, and "Ask AI"
MAANG+ Interview Prep
AI Mock Interviews simulate every technical loop at top companies
Free Resources
