Genetic Algorithms in Elixir

INTERACTIVE COURSE

Genetic Algorithms in Elixir

Beginner

74 Lessons

25h 30min

Certificate of Completion

Genetic Algorithms in Elixir

52 Playgrounds

16 Quizzes

97 Illustrations

Takeaway Skills

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.

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

Course Content

1

Introduction

A Guide to Follow This Course What to expect from this course?

2

Writing Your First Genetic Algorithm

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

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

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

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

7

Generating New Solutions

7 Lessons

8

Preventing Premature Convergence

5 Lessons

9

Replacing and Transitioning

5 Lessons

10

Tracking Genetic Algorithms

6 Lessons

11

Visualizing the Results

4 Lessons

12

Optimizing Your Algorithms

7 Lessons

13

Writing Tests and Code Quality

4 Lessons

14

Moving Forward

4 Lessons

COURSE AUTHOR

How You'll Learn

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