# Genetic Algorithms in Python and MATLAB – Free Udemy Courses

## Genetic Algorithms in Python and MATLAB â€“ Free Udemy Courses

### A Practical and Hands-on Approach

#### What youâ€™ll learn

Genetic Algorithms in Python and MATLAB â€“ Free Udemy Courses

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

#### Requirements

• Basic Math and Optimization

• Python Programming

• MATLAB Programming

#### Description

Genetic Algorithms (GAs) are members of a general class of optimization algorithms, known as Evolutionary Algorithms (EAs), which simulate a fictional environment based on the theory of evolution to deal with various types of a mathematical problems, 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 the 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 tutorial, 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 optimization problems.

#### Who this course is for:

• 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

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