Optimization Using Genetic Algorithms : MATLAB Programming

Optimization Using Genetic Algorithms : MATLAB Programming, available at $39.99, has an average rating of 3.8, with 23 lectures, 1 quizzes, based on 75 reviews, and has 7731 subscribers.

You will learn about Implementation of Genetic Algorithm in MATLAB Analyze the performance of the Genetic Algorithm Modify or improve the Genetic Algorithm Specifying objective functions Specifying constraints Vectorizing objective function and constraints Obtaining local and global optima This course is ideal for individuals who are Anyone who is interested to solve optimization problems. or Researchers who want to publish ISI papers in this field. or Students who are working on optimization problems. It is particularly useful for Anyone who is interested to solve optimization problems. or Researchers who want to publish ISI papers in this field. or Students who are working on optimization problems.

Enroll now: Optimization Using Genetic Algorithms : MATLAB Programming

Summary

Title: Optimization Using Genetic Algorithms : MATLAB Programming

Price: $39.99

Average Rating: 3.8

Number of Lectures: 23

Number of Quizzes: 1

Number of Published Lectures: 23

Number of Published Quizzes: 1

Number of Curriculum Items: 24

Number of Published Curriculum Objects: 24

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Implementation of Genetic Algorithm in MATLAB
Analyze the performance of the Genetic Algorithm
Modify or improve the Genetic Algorithm
Specifying objective functions
Specifying constraints
Vectorizing objective function and constraints
Obtaining local and global optima

Who Should Attend

Anyone who is interested to solve optimization problems.
Researchers who want to publish ISI papers in this field.
Students who are working on optimization problems.

Target Audiences

Anyone who is interested to solve optimization problems.
Researchers who want to publish ISI papers in this field.
Students who are working on optimization problems.

There has been a rapidly growing interest in a field called Genetic Algorithms during the last thirty years. Have you ever wondered how specific theories greatly inspire a particular invention?. The same goes with Genetic Algorithms. All of us would have heard of the famous view of Charles Darwin, “Survival of the fittest”, which extends to Evolution by Natural Selection. Inspired by Darwin’s theory, the Genetic Algorithm is a part of Evolutionary Algorithms, specifically to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover, and selection. The Genetic Algorithm can be easily applied to different applications, including Machine Learning, Data Science, Neural Networks, and Deep Learning.

This course will teach you to implement genetic algorithm-based optimization in the MATLAB environment, focusing on using the Global Optimization Toolbox. Various kinds of optimization problems are solved in this course. At the end of this course, you will implement and utilize genetic algorithms to solve your optimization problems. The complete MATLAB programs included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Take advantage of learning and understanding the fast-growing field of evolutionary computation.

Happy learning.

Course Curriculum

Chapter 1: Introduction to Optimization

Lecture 1: Welcome to the course

Lecture 2: Local vs. Global Optima

Lecture 3: Single local solution, Multiple local solutions and Single global solution

Chapter 2: Objective of Fitness Functions

Lecture 1: What is objective function?

Lecture 2: MATLAB Script For Single Objective Function

Lecture 3: MATLAB Script For Vectorized Function Call

Lecture 4: Passing Extra Parameters, Fixed Variables, or Data in the Objective Functions

Chapter 3: Introduction to Genetic Algorithm

Lecture 1: What is Genetic Algorithm?

Lecture 2: Fitness/Objective Functions, Individuals/genome and Score

Lecture 3: Populations and Generations

Lecture 4: Diversity

Lecture 5: Fitness Values, Best Fitness Values, Parents, Children

Lecture 6: How Genetic Algorithm Works?

Chapter 4: Genetic Algorithm in MATLAB

Lecture 1: Minimize the Unconstrained Function

Lecture 2: Minimize a Function with Linear Inequality Constraints

Lecture 3: Minimize a Nonsmooth Function with Linear Equality and Inequality Constraints

Lecture 4: Optimize with Linear Constraints and Bounds

Lecture 5: Optimize with Nonlinear Constraints

Lecture 6: Minimize a Nonlinear Function with Integer Constraints

Lecture 7: Obtain the Solution and Function Value

Lecture 8: Global Minima Using ga

Lecture 9: Vectorized Fitness Function

Lecture 10: Maximizing an Objective Function

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

Rating Distribution

1 stars: 3 votes
2 stars: 1 votes
3 stars: 13 votes
4 stars: 21 votes
5 stars: 37 votes

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