Genetic Algorithm & Simulated Annealing in C++

This online course is for students and software developers who want to level up their skills by learning an interesting optimization algorithm in C++.

You will learn two of the most famous AI algorithms by writing it in C++ from scratch, so we will not use any libraries. Plus, you will see a less known one: Differential Evolution

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

  • The knapsack problem

• Differential Evolution:

  • Theory and different strategies

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

Sign up now and let's get started!