Optimization Using Genetic Algorithm in MATLAB

Optimization Using Genetic Algorithm in MATLAB, available at $44.99, has an average rating of 4, with 30 lectures, based on 178 reviews, and has 1501 subscribers.

You will learn about 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 This course is ideal for individuals who are Undergraduate, Post graduate Students and PhD scholars of all Engineering disciplines It is particularly useful for Undergraduate, Post graduate Students and PhD scholars of all Engineering disciplines.

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Summary

Title: Optimization Using Genetic Algorithm in MATLAB

Price: $44.99

Average Rating: 4

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $19.99

Quality Status: approved

Status: Live

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

Who Should Attend

Undergraduate, Post graduate Students and PhD scholars of all Engineering disciplines

Target Audiences

Undergraduate, Post graduate Students and PhD scholars of all Engineering disciplines

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)

Course Curriculum

Chapter 1: Basics of MATLAB Programming

Lecture 1: Introduction to MATLAB

Lecture 2: Variables and Operators

Lecture 3: Vector Declaration

Lecture 4: Indexing and Size of Vectors

Lecture 5: Matrix Operations

Lecture 6: max(), min(), ones(), and zeros()

Lecture 7: rand(), randi(), and repmat()

Lecture 8: If Statement

Lecture 9: For Loop

Lecture 10: While Loop

Lecture 11: Functions

Lecture 12: Plots

Chapter 2: Concepts of Optimization and Genetic Algorithm

Lecture 1: Introduction to Optimization

Lecture 2: Effects of Constraints on Optimal Solution

Lecture 3: Terminologies Used in Optimization and Pseudo Codes

Lecture 4: Inspiration of Genetic Algorithm

Lecture 5: Step I – Selection

Lecture 6: Step II and Step III – Crossover and Mutation

Lecture 7: Step IV – Evaluation

Chapter 3: MATLAB Implementation of GA to Solve Benchmark Functions

Lecture 1: Matyas Function Introduction

Lecture 2: Coding for Matyas Function Part I – Problem Formulation

Lecture 3: Coding for Matyas Function Part II – Initialization

Lecture 4: Coding for Matyas Function Part III – Crossover

Lecture 5: Coding for Matyas Function Part IV – Mutation

Lecture 6: Coding for Matyas Function Part V – Plotting Results

Lecture 7: Coding for Matyas Function Part VI – Understanding Results

Lecture 8: Rosenbrock Function Introduction

Lecture 9: Coding for Rosenbrock Function

Chapter 4: Capstone Project

Lecture 1: Capstone Project Introduction

Lecture 2: Capstone Project Coding

Instructors

Mayank Dadge
B.Tech Mechanical Engineering
Dr. H. T. Jadhav
B.E. (Power Engg.), M.Tech (Power Engg.), Ph.D.(Power Engg.)

Rating Distribution

1 stars: 4 votes
2 stars: 1 votes
3 stars: 12 votes
4 stars: 43 votes
5 stars: 118 votes

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