Introduction to Genetic Algorithms: Theory and Applications
What you'll learn
- Use the Genetic Algorithm to solve optimization problems
- Modify or improve the Genetic Algorithm
- Analyze the performance of the Genetic Algorithm
- Be familiar with the basics of programming
- Be familiar with Matlab programming language
This is an introductory course to the Genetic Algorithms. We will cover the most fundamental concepts in the area of nature-inspired Artificial Intelligence techniques. Obviously, the main focus will be on the Genetic Algorithm as the most well-regarded optimization algorithm in history. The Genetic Algorithm is a search method that can be easily applied to different applications including Machine Learning, Data Science, Neural Networks, and Deep Learning.
With over 10 years of experience in this field, I have structured this course to take you from novice to expert in no time. Each section introduces one fundamental concept and takes you through the theory and implementation. The course is concluded by solving several case studies using the Genetic Algorithm.
Most of the lectures come with coding videos. In such videos, the step-by-step process of implementing the optimization algorithms or problems are presented. We have also a number of quizzes and exercises to practice the theoretical knowledge covered in the lectures.
Here is the list of topics covered:
The inspiration of the Genetic Algorithm
Selection or survival of the fittest
Recombination or crossover
I am proud of 200+ 5-star reviews. Some of the reviews are as follows:
Femi said: "I really enjoyed the instructor style of explanation. He has a very solid understanding of the course and he made complex problems fun to solve."
Abhay said: "I tread cautiously when am taking MOOCs, as most of the courses offered fall short of what they claim they have to offer, eventually ending up quitting the course after making, say, 10% progress. But this course is different, after completing almost 80% - 90% of the course, I can say with confidence, that this is one of the most worthy courses I have undertaken on the topic of GA. The course is precise, relevant to the real-world problems and Ali is quite an engaging and prompt instructor. Hell, even the Possums in Australia would double that."
Ahmad said: "It was very nice experience. I think the course is designed very well for beginners like me because it starts with the basics. Then it gradually becomes more difficults. Overall it was a great course. Thanks Ali or Seyedali both of you :D :) if you know what I mean."
Join 1000+ students and start your optimization journey with us. If you are in any way not satisfied, for any reason, you can get a full refund from Udemy within 30 days. No questions asked. But I am confident you won't need to. I stand behind this course 100% and am committed to help you along the way.
Who this course is for:
- This course is for everyone who wants to learn about optimization techniques
- Both beginners and experts in Artificial Intelligence will benefit from this course since it covers both theory and application from basics to advanced topics
- Those who wants to solve challenging optimization problems
- Those curious about how the Genetic Algorithm solves optimization problems as one of the best and most well-regarded AI techniques in the world
Professor Seyedali (Ali) Mirjalili is internationally recognized for his advances in Artificial Intelligence (AI) and optimization, including the first set of SI techniques from a synthetic intelligence standpoint - a radical departure from how natural systems are typically understood - and a systematic design framework to reliably benchmark, evaluate, and propose computationally cheap robust optimization algorithms. Prof. Mirjalili has published over 150 journal articles, many in high-impact journals, with one paper having over 4000 citations - the most cited paper in the Elsevier Advances in Engineering Software journal. In addition, he has more than five books, 30 book chapters, and 15 conference papers.
Prof. Mirjalili has over 50,000 citations in total with an H-index of 80. From Google Scholar metrics, he is globally one of the most-cited researchers in Artificial Intelligence. As the most cited researcher in Robust Optimization, he is in the list of 1% highly-cited researchers and named as one of the most influential researchers in AI by the world by Web of Science.
Ali is a senior member of IEEE and an associate editor of several journals including IEEE Access, Applied Soft Computing, Advances in Engineering Software, and Applied Intelligence. His research interests include Robust Optimization, Engineering Optimization, Multi-objective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is working on the application of multi-objective and robust meta-heuristic optimization techniques as well.
In addition to his excellent research outputs, Prof. Ali has been a teacher for over 15 years and a Udemy instructor for more than three years. He has 10,000+ students, and the majority of his courses have been highly ranked by both Udemy and students. He is the only Udemy instructor in the list of top 1% highly-cited researchers.