Optimization Using Genetic Algorithm in MATLAB

MATLAB programming basics and GA implementation to solve constrained optimization problems in the field of engineering

4.00 (174 reviews)
Udemy
platform
English
language
Engineering
category
instructor
Optimization Using Genetic Algorithm in MATLAB
1,495
students
2.5 hours
content
Jul 2022
last update
$44.99
regular price

What you will learn

Write MATLAB program to solve Engineering problems

Understand the workings of Genetic Algorithm

Implement Genetic Algorithm to solve benchmark functions

Implement Genetic Algorithm to solve an engineering optimization problems

Design and develop MATLAB program using GA for an engineering optimization problems

Work on research problem leading to publication in international journals of high repute

Why take this course?

This course is specifically developed for B. Tech. and M. Tech/MS students of all Engineering disciplines. Especially the students of Mechanical, Electrical, Automobile, Chemical, Aeronautical, Electronics, Computer science, Instrumentation, Mechatronics, Manufacturing, Robotics and Civil Engineering can learn MATLAB basics and solve Engineering Optimization problems in their area as part of mini-project or capstone project. In addition to this, the course is also useful to Ph. D. students of different engineering branches. The course is designed in such a way that the student who is not well versed with MATLAB programing can learn the basics of MATLAB in the first part so that it is easy for him/her to understand MATLAB implementation of Genetic Algorithm to solve simple and advanced Engineering problems. The content is so organized that the learner should be able to understand Engineering optimization from scratch and solve research problems leading to publication in an international journal of high repute. It should be useful to students of all universities around the world. 

This course is divided into FOUR Parts

  • Part I - Basics of MATLAB Programming

  • Part 2 - Concept of Genetic Algorithm

  • Part 3 - MATLAB Implementation of GA to solve benchmark functions

  • Part 4 - Capstone Project (MATLAB Implementation of GA to solve a typical Engineering optimization Problem)

Screenshots

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Reviews

Bibhavari
December 30, 2022
This is the best course ever. It solved all my problems. I was stress-free and relaxed because of the clarity of instructions in the course. I could understand the concept very well and implement it smoothly with no problems. This is bestest course ever. Get to see more courses from these instructors.
Ahmet
December 25, 2022
Dear Dr. H. T. Jadhav, Thank you for your reply. I continue to adapt the change to my own problem. Thank you again for your interest and feedback. Best Regards.
Ray
December 22, 2022
What I loved about this course are the examples that were chosen. They are simple enough to not get bogged down in the details but still made the mechanics of the GA clear. The capstone was excellent, also, in that it left plenty of room for experimentation by the student to get a feel for how the GA parameters affect the execution of the algorthm.
Muhammad
December 6, 2022
A good online course is engaging and challenging. It invites students to participate, motivates them to contribute and captures their interest and attention
Mohini
September 22, 2022
A good online course is engaging and challenging. It invites students to participate, motivates them to contribute and captures their interest and attention
Atharva
July 19, 2022
It was a great course! Everything is very well-structured and thoroughly explained, with many examples to understand how and when to use every tool. I enjoyed it and will definitely recommend this course to everyone who is interested in learning Optimization Using Genetic Algorithm in MATLAB from scratch
Soham
July 17, 2022
The course is very helpful in understanding the Genetic algorithm and the implementation of Matlab. The instructors are well qualified and share their insights along with the course material! I strongly recommend this course!!

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4784796
udemy ID
7/16/2022
course created date
7/16/2022
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