HomeCoursesGenetic Algorithms in Elixir

Beginner

25h 30min

Genetic Algorithms in Elixir
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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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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
This course has been designed to introduce you to a field of programming you might have never been exposed to. In this course, y...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.
Learn about the basics of Genetic Algorithms in Elixir.

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Course Content

1.

Introduction

3 Lessons

Get familiar with Genetic Algorithms in Elixir, their applications, and Elixir's advantages.

5.

Evaluating Solutions and Populations

6 Lessons

Take a closer look at optimizing cargo loads, penalty functions, termination criteria, and crafting fitness functions.

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.

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.
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