Description
Introduction to Genetic Algorithms: Theory and Applications is the name of a training course in the field of Genetic Algorithm that teaches the main mechanism of this search technique as a detector or optimization search algorithm in MATLAB. Genetic algorithm is a method used in computer science which is used to find an approximate solution to optimize the model and research problems. This method can be applied to a variety of applications including machine learning, data science, neural networks, and deep learning.
In this course, you will become familiar with the genetic algorithm and learn the most important concepts about artificial intelligence techniques. The main emphasis of this course is on the genetic algorithm method as it is the best optimization algorithm in computer science. By watching each video, you can see the step-by-step process of implementing the genetic algorithm and learn exactly how to fix the problems.
Courses taught in this course:
- Use the genetic algorithm to solve optimization problems
- Modify and improve the genetic algorithm
- Performance analysis of genetic algorithms
Introduction to genetic algorithms: theory and applications:
- English language
- Duration: 7 hours and 5 minutes
- Number of lessons: 26
- Level of education: Average
- Instructor: Prof. Seyedali Mirjalili
- File format: mp4
Course topics
26 lectures 07:05:53
Inspiration
1 conference 19:00
Representation of genes and fitness function
2 lessons 33:32
Selection
4 lectures 01:16:58
Recombination (crossover)
3 lessons 59:27
Mutation
2 lessons 19:37
Elitism
4 lessons 42:04
Genetic Algorithm Implementation
2 lessons 46:29
Continuous version of the genetic algorithm
5 lectures 01:23:09
Genetic Algorithm Applications
2 lessons 43:57
bonus video
1 conference 01:40
Course prerequisites
- Know the basics of programming
- Know the Matlab programming language
Pictures
sample movie
Installation guide
After ripping, watch with your favorite player.
english subtitle
Quality: 720p
Download link
File password(s): ngaur.com
Cut
7.31 GB