Udemy
UdemyGenetic Algorithms in Python and MATLAB
4.6(595)27k studentsALL LEVELSEnglish
Korshub's take
From Evolution Theory to Working Code
Genetic Algorithms (GAs) belong to the broader family of Evolutionary Algorithms, optimization methods that simulate a simplified model of biological evolution to tackle optimization problems, and more broadly sit within the class of Metaheuristics used for optimization and unsupervised learning.
Taught by Dr. Mostapha Kalami Heris, this video series moves from theory straight into implementation, building both a Binary Genetic Algorithm and a Real-coded Genetic Algorithm from scratch, line by line, in both Python and MATLAB.
What's Covered
- Initialization and parent selection
- Crossover and mutation operators
- Sorting and selection strategies
Each concept is paired with hands-on code, so the theory never stays abstract. By the end, students should be comfortable enough with evolutionary computation to build a genetic algorithm from the ground up and apply it to their own optimization problems, a useful skill in computational intelligence, engineering, or applied math.
About this course
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.
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