Genetic Algorithm & Simulated Annealing in C++

[mitsumi]

Genetic Algorithm & Simulated Annealing in C++
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 1.16 GB
Genre: eLearning Video | Duration: 20 lectures (1 hour, 49 mins) | Language: English
 Study these machine learning/optimization algorithms in continuous problems and the Travelling Salesperson Problem (TSP)

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

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.

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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