Binary Genetic Algorithm in MATLAB - Part B - Practical Genetic Algorithms Series

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.
Topics covered in this part are listed below:
● Perform Mutation
● Merging, Sorting and Selection
● Merging, Sorting and Selection
● Finalizing and Running GA
● Other Crossover Operators
For more information and download project files for this tutorial, see: yarpiz.com/ypga191215
Seven parts of this video tutorial, is available via following links:
Part 1 - Introduction to Genetic Algorithms: • Introduction to Geneti...
Part 2 - Binary Genetic Algorithm in MATLAB (A): • Binary Genetic Algorit...
Part 3 - Binary Genetic Algorithm in MATLAB (B): [Current Part]
Part 4 - Binary Genetic Algorithm in MATLAB Part (C): • Binary Genetic Algorit...
Part 5 - Real-Coded Genetic Algorithm in MATLAB: • Real-Coded Genetic Alg...
Part 6 - Genetic Algorithm in Python - Part A: • Genetic Algorithm in P...
Part 7 - Genetic Algorithm in Python (B): • Genetic Algorithm in P...
Publisher: Yarpiz (www.yarpiz.com)
Instructor: Mostapha Kalami Heris

[@anhuynhduy8459]

Great lessons!!! Hope you will have another video that relate to apply GA into reality such as tunning PID parameters.

[@khurshidaanjum6150]

Gr8 sir...your GA tutorials are best among all contents on RUclips....well explained...👍

[@mrbrightside0076]

Thanks for the video!

[@ronaldescobar5253]

Muchas Gracias!!...excelente

[@pedromnunes]

thank you again

[@SouadMath]

It is very nice

[@rezanaiemaee5424]

its perfect .. how can we use benchmark function like ackley for fitnessfunction??

[@sibasishdhibar9173]

its great helpfull sir

[@YarpizProjects]

Thank you very much.

[@bohdanschatschneider9962]

How did he do the breakpoint at 11:38? Any help would be appreciated.

[@bohdanschatschneider9962]

I figured it out: 1) left click on the number line at line 39 to get the red "breakpoint" circle. 2) click on the app1 tab. 3) click the run button or hit F5 (at this point it will take you back to line 39 on the RunGA tab with a green arrow next to the red circle). 4) run all of the 'pop' games that he does. Enjoy.