C++ Machine Learning Algorithms Inspired by Nature

About C++ Machine Learning Algorithms Inspired by Nature

This online course is for students and software developers who want to

level up

their skills by learning interesting optimization algorithms in C++. You will

learn some of the most famous AI algorithms

by writing it in C++

from scratch

, so we will not use any libraries. We will start with the

Genetic Algorithm (GA)

, continue with

Simulated Annealing (SA)

and then touch on a

less known one: Differential Evolution.

Finally, we will look at

Ant Colony Optimization (ACO).

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. However, nature gives us fascinating sources of inspiration, such as the behaviour of ants, so that Ant Colony Optimization is an interesting algorithm as well. We will solve

continuous problems

(find the maximum/minimum of a continuous function) and discrete problems, such as the

Travelling Salesperson Problem

(TSP), where you have to find the shortest path in a network of cities, or the

Knapsack Problem.

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

The knapsack problem

Differential Evolution:

Theory and different strategies

Code example on one strategy, the standard one (DE/rand/1/bin)

Ant Colony Optimization:

Theory and Inspiration

Example on the Travelling Salesperson Problem

Sign up now and let's get started!