Genetic Algorithms in Python and MATLAB

Genetic Algorithms in Python and MATLAB, available at Free, has an average rating of 4.5, with 42 lectures, 1 quizzes, based on 576 reviews, and has 27084 subscribers.

You will learn about How genetic algorithms work? Binary and Real-Coded Genetic Algorithms Implementation of GA in Python and MATLAB This course is ideal for individuals who are Computer Science Students or Engineering and Applied Math Students or Anyone interested in Optimization or Anyone interested in Computational Intelligence or Anyone interested in Metaheuristics or Anyone interested in Evolutionary Computation It is particularly useful for Computer Science Students or Engineering and Applied Math Students or Anyone interested in Optimization or Anyone interested in Computational Intelligence or Anyone interested in Metaheuristics or Anyone interested in Evolutionary Computation.

Enroll now: Genetic Algorithms in Python and MATLAB

Summary

Title: Genetic Algorithms in Python and MATLAB

Price: Free

Average Rating: 4.5

Number of Lectures: 42

Number of Quizzes: 1

Number of Published Lectures: 42

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 42

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

How genetic algorithms work?
Binary and Real-Coded Genetic Algorithms
Implementation of GA in Python and MATLAB

Who Should Attend

Computer Science Students
Engineering and Applied Math Students
Anyone interested in Optimization
Anyone interested in Computational Intelligence
Anyone interested in Metaheuristics
Anyone interested in Evolutionary Computation

Target Audiences

Computer Science Students
Engineering and Applied Math Students
Anyone interested in Optimization
Anyone interested in Computational Intelligence
Anyone interested in Metaheuristics
Anyone interested in Evolutionary Computation

Genetic Algorithms (GAs) are members of a general class of optimization algorithms, known as Evolutionary Algorithms (EAs), which simulate a fictional environment based on theory of evolution to deal with various types of mathematical problem, especially those related to optimization. Also Genetic Algorithms can be categorized as a subset of Metaheuristics, which are general-purpose tools and algorithms to solve optimization and unsupervised learning problems.

In this series of video tutorials, we are going to learn about Genetic Algorithms, from theory to implementation. After having a brief review of theories behind EA and GA, two main versions of genetic algorithms, namely Binary Genetic Algorithm and Real-coded Genetic Algorithm, are implemented from scratch and line-by-line, using both Python and MATLAB. This course is instructed by Dr. Mostapha Kalami Heris, who has years of practical work and active teaching in the field of computational intelligence.

Components of the genetic algorithms, such as initialization, parent selection, crossover, mutation, sorting and selection, are discussed in this tutorials, and backed by practical implementation. Theoretical concepts of these operators and components can be understood very well using this practical and hands-on approach.

At the end of this course, you will be fully familiar with concepts of evolutionary computation and will be able to implement genetic algorithms from scratch and also, utilize them to solve your own optimization problems.

Course Curriculum

Chapter 1: Introduction to Genetic Algorithms

Lecture 1: Introduction

Lecture 2: What is an Evolutionary Algorithm?

Lecture 3: What is a Genetic Algorithm?

Lecture 4: Crossover

Lecture 5: Mutation

Lecture 6: Parent Selection

Lecture 7: Merging, Sorting and Selection

Chapter 2: Binary Genetic Algorithm in MATLAB

Lecture 1: Problem Definition and Structure of GA Code

Lecture 2: Initialization

Lecture 3: Keeping Track of Best Solution Ever Found

Lecture 4: The Main Loop

Lecture 5: Selecting Parents

Lecture 6: Performing Crossover

Lecture 7: Performing Mutation

Lecture 8: Merging, Sorting and Selection

Lecture 9: Finalizing and Running GA

Lecture 10: Other Crossover Operators

Lecture 11: Roulette Wheel Selection

Lecture 12: Calculating Selection Probabilities

Lecture 13: Finalizing the GA Code

Chapter 3: Real-Coded Genetic Algorithm

Lecture 1: Real-Valued or Continuous Optimization Problems

Lecture 2: Crossover in Continous Domain

Lecture 3: Mutation in Continous Domain

Lecture 4: Real-Coded Genetic Algorithm in MATLAB

Lecture 5: Implementing Real-Coded Crossover and Mutation

Lecture 6: Finalizing MATLAB Implementation of Real-Coded GA

Lecture 7: Improving Crossover

Lecture 8: Taking Care of Decision Variable Bounds

Chapter 4: Genetic Algorithms in Paython

Lecture 1: Structure of GA Code in Python

Lecture 2: The Main Function of GA

Lecture 3: Initialization

Lecture 4: Keeping Track of Best Solution Ever Found

Lecture 5: The Main Loop

Lecture 6: Selecting Parents

Lecture 7: Performing Crossover

Lecture 8: Performing Mutation

Lecture 9: Taking Care of Decision Variable Bounds

Lecture 10: Evaluation and Comparison

Lecture 11: Merging, Sorting and Selection

Lecture 12: Finalizing and Running GA

Lecture 13: Roulette Wheel Selection

Lecture 14: Using Different Variable Ranges

Instructors

Yarpiz Team
Academic Education and Research Group
Mostapha Kalami Heris
Programmer and Instructor

Rating Distribution

1 stars: 8 votes
2 stars: 10 votes
3 stars: 62 votes
4 stars: 192 votes
5 stars: 304 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu