MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.76 GB | Duration: 2h 50m
Study the Genetic Algorithm, Simulated Annealing, Ant Colony Optimization, Differential Evolution by Coding from Scratch
What you’ll learn
Genetic Algorithm in C++
Simulated Annealing
Differential Evolution
Ant Colony Optimization
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
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!
Who this course is for:
Students and software developers who want to learn interesting algorithms
Anyone interested in metaheuristic algorithms
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