Binary Genetic Algorithm in MATLAB - Part C - 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:
● Roulette Wheel Selection
● Calculating Selection Probabilities
● Finalizing the GA Code
For more information and download project files for this tutorial, see: yarpiz.com/ypg...
Other parts of this video tutorial series are 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): • Binary Genetic Algorit...
Part 4 - Binary Genetic Algorithm in MATLAB (C): [Current Part]
Part 5 - Real-Coded Genetic Algorithm in MATLAB: • Real-Coded Genetic Alg...
Part 6 - Genetic Algorithm in Python (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

[@LEARNWITHPANDA]

*Binary genetic algorithm is very useful to solve continuous optimization problems. Good tutorial. Thanks Yarpiz*

[@xiaoweijie952]

Thanks a lot, Mr. Mostapha, you are doing a really amazing job. Your tutorial video really helped my mathematical modeling competition. I hope you could update videos about immune algorithm, which is highly related with GA.

[@kdSU30]

Thank you Sir for this fantastic series on GA. I have one query though. During random selection of parents, you have ensured that both parents are compulsorily different before crossover happens. However, during probabilistic selection of parents, you are not enforcing that criteria, which can result in selection of same parent twice for crossover, which will give two offsprings which are same as parents. I may be wrong, your comment is appreciated.

[@princessmaram5397]

Thank you Dr. Mostefa for this wonderful video. Could you please do a video about artificiel bee colony?

[@Vaka_meb]

thank you a looooooot , you really helped me improve my coding skills , you're doin a great job ❤❤

[@priyajitbhattacharya81]

Thanks a lot... Can u pls guide how to display the best solution along with best cost

[@loukrizabdelouadoud5042]

great work , thanks doc

[@sebastianhelm8792]

Great Job!!! Thank you

[@alejandromartinezleon877]

Thanks for the videos. I have a doubt. I tried to calculate the Selection Probabilities with the following cost values [-5,-9,4,0,-6,-10,12,-7]. And I got [1.48858081e-01, 3.24332409e-02, 4.58967642e+00, 1.00000000e+00, 1.01701392e-01, 2.21587282e-02, 9.66821285e+01, 6.94834512e-02]. I expected -10 has the higher probability but it is not the case and the reason is because avg_cost

[@kdSU30]

Kindly see my recent comment.

[@bijendrarawat836]

this one is good but sir ,could you explain real-value genetic algorithms

[@user-huss-8362]

Good job, can you send this work (m.file) for the benfit of it ...thank you

[@Abdulghani-ub8fj]

you can find it on the website attached in the description