What you'll learn

The genetic algorithm & simulated annealing in C++

Genetic algorithm & simulated annealing on a continuous problem

Genetic algorithm on the travelling salesperson problem (TSP)

Requirements

Understand basic C++ and you should have a C++ IDE (any, I am using Visual Studio)

An understanding of some mathematics

An understanding of general algorithmics

An interest in cool algorithms :)

Description

You will learn two of the most famous AI algorithms by writing it in C++ from scratch, so we will not use any libraries.

The Genetic Algorithm is the most famous one in a class called metaheuristics or optimization algorithms. You will learn what optimization algorithms are, when to use them, and then you will solve two problems with the Genetic Algorithm(GA). The second most famous one is Simulated Annealing.

These problems are: a continuous problem(find the maximum/minimum of a continuous function) and the Travelling Salesperson Problem (TSP), where you have to find the shortest path in a network of cities.

Prerequisites:

understand basic C++

any C++ IDE (I am using Visual Studio)

understanding of algorithms

understand mathematics

I recommend that you do the examples yourself, instead of passively watching the videos.

Here's a brief outline of what you will learn:

What optimization algorithms are

Genetic Algorithm theory:

General structure

How crossover is done

How mutation is done

Genetic Algorithm on a continuous problem:

Challenges particular to continuous problems: decoding the bits ("chromosomes") into a float value

Crossover: tournament selection and single point crossover

Mutation

Genetic Algorithm on the TSP (Travelling Salesperson Problem):

Creating a fitness function for the TSP

Challenge particular to this problem: how to do crossover?

Mutation

Simulated Annealing:

Basic Theory

Optimizing Himmelblau's function

Sign up now and let's get started!

Who this course is for:

Students and software developers who want to learn interesting algorithms

Anyone interested in this metaheuristic algorithm



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