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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

Course Overview

What You'll Learn

Course Content

Apply Your Skills

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.

Learn about the basics of Genetic Algorithms in Elixir.

Course Content

Introduction

3 Lessons

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?

Writing Your First Genetic Algorithm

6 Lessons

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

Breaking Down Genetic Algorithms

6 Lessons

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

Encoding Problems and Solutions

6 Lessons

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

Evaluating Solutions and Populations

6 Lessons

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

Selecting the Best

5 Lessons

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

Exploring Selection

Customizing Selection in Your Framework

Implementing Common Selection Strategies

Tournament Selection

Roulette Selection

Generating New Solutions

7 Lessons

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

Introducing N-Queens

Solving N-Queens with Order-One Crossover

Exploring Crossover

Uniform Crossover

Whole Arithmetic Recombination

Crossing Over More Than Two Parents

Implementing Chromosome Repairment

Preventing Premature Convergence

5 Lessons

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

Breaking Codes with Genetic Algorithms

Understanding Mutation

Customizing Mutation in Your Framework

Implementing Common Mutation Strategies

Other Methods to Combat Convergence

Replacing and Transitioning

5 Lessons

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

Creating a Class Schedule

Understanding Reinsertion

Implementing Reinsertion Strategies

Experimenting with Reinsertion

Different Populations in Genetic Algorithms

10.

Tracking Genetic Algorithms

6 Lessons

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

Using Genetic Algorithms to Simulate Evolution

Logging Statistics Using ETS

Tracking Statistics in Your Framework

Finding the Average Tiger

Tracking Genealogy in a Genealogy Tree

Tracking Genealogy in Your Framework

11.

Visualizing the Results

4 Lessons

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

Visualizing the Genealogy of the Tiger Evolution

Visualizing Basic Statistics

Playing Tetris with Genetic Algorithms

Creating and Running the Tetris Game

12.

Optimizing Your Algorithms

7 Lessons

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

Benchmarking and Profiling Genetic Algorithms

Using Benchee to Analyze Performance

Identifying Bottlenecks with ExProf

Writing Fast Elixir

Improving Performance with Parallelization

Treating Chromosomes as Processes

Improving Performance with NIFs

13.

Writing Tests and Code Quality

4 Lessons

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

Understanding Randomness

Writing Property Tests with ExUnit

Cleaning Up Your Framework

Writing Type Specifications

14.

Moving Forward

4 Lessons

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

Genetic Algorithms Role in AI

Genetic Algorithms in Real Life Applications

Evolving Neural Networks

Where to Go Next

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