Udemy Free: Genetic algorithms course with Python and MATLAB

Genetic algorithms are random search algorithms that have been developed in an effort to mimic the mechanics of natural selection and natural genetics.

They operate on string structures, as biological structures, that evolve over time according to the rule of survival of the fittest through the use of a randomly structured exchange of information.

Therefore, in every generation, a new set of strings is created, using parts of the fittest members of the old set.

The main characteristics of a genetic algorithm are the following:

  • The genetic algorithm works with a coding of the set of parameters, not with the parameters themselves.
  • Start your search from a population of points, not single point.
  • The genetic algorithm uses payment information, no derivatives.
  • The genetic algorithm uses probabilistic transition rules, non-deterministic.

Genetic algorithms in Python and MATLAB

A practical approach

Who is this course for?

  • Computer Science Students
  • Engineering and applied mathematics students
  • Anyone interested in optimization
  • Interested in computational intelligence
  • Anyone interested in metaheuristics
  • Anyone interested in Evolutionary Computing


  • Optimization and basic math
  • Python programming
  • MATLAB programming

What you will learn

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

This course is available for free without the need for any coupon, through the "FREE" option.

We recommend that you first read the differences between a free and a paid course to avoid misunderstandings:

Free courses

  • Online video content

Paid courses

  • Online video content
  • Certificate of completion
  • Instructor Questions and Answers
  • Direct message to the instructor

To obtain the course for free, click on the following button:

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