Udemy - Introduction to Genetic Algorithms: Theory and Applications [Course Drive]

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  • TypeTutorials
  • LanguageEnglish
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  • Last checkedMay. 16th '20
  • Date uploadedMay. 15th '20
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Introduction to Genetic Algorithms: Theory and Applications

Learn the main mechanisms of Genetic Algorithm as a heursitic Artificial Intalligence search or optimization in Matlab






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

Requirements

• Be familiar with the basics of programming
• Be familiar with Matlab programming language


Description

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.

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





Files:

Introduction to Genetic Algorithms - Theory and Applications Introduction to Genetic Algorithms - Theory and Applications 7. Implementation of the Genetic Algorithm
  • 1. Coding Genetic Algorithm in Matlab.mp4 (747.3 MB)
  • 1. Coding Genetic Algorithm in Matlab.srt (42.8 KB)
  • 1.1 GA_example.zip.zip (3.0 KB)
  • 2. Developing a GA function in Matlab.mp4 (242.8 MB)
  • 2. Developing a GA function in Matlab.srt (14.2 KB)
  • 2.1 GA_function.zip.zip (3.0 KB)
  • ReadMe.txt (0.2 KB)
  • Visit Coursedrive.org.url (0.1 KB)
    • 1. Inspiration
    • 1. Inspiration.mp4 (208.5 MB)
    • 1. Inspiration.srt (21.4 KB)
    2. Gene Representation and Fitness Function
    • 1. Gene Representation and Fitness Function Theory.mp4 (193.7 MB)
    • 1. Gene Representation and Fitness Function Theory.srt (20.2 KB)
    • 2. Gene Representation and Fitness Function Coding.mp4 (383.8 MB)
    • 2. Gene Representation and Fitness Function Coding.srt (20.7 KB)
    3. Selection
    • 1. Selection Using Roulette Wheel Theory.mp4 (116.8 MB)
    • 1. Selection Using Roulette Wheel Theory.srt (21.5 KB)
    • 2. Selection coding an example.mp4 (93.8 MB)
    • 2. Selection coding an example.srt (19.1 KB)
    • 3. Selection coding a roulette wheel function.mp4 (182.2 MB)
    • 3. Selection coding a roulette wheel function.srt (31.2 KB)
    • 3.1 selection.m.m (1.2 KB)
    • 4. Fitness Scaling in The Roulette Wheel.mp4 (366.5 MB)
    • 4. Fitness Scaling in The Roulette Wheel.srt (22.7 KB)
    4. Recombination (crossover)
    • 1. Recombining (Crossovering) Chromosome Theory.mp4 (175.3 MB)
    • 1. Recombining (Crossovering) Chromosome Theory.srt (18.4 KB)
    • 2. Recombining (Crossovering) Genes in Chromosomes Coding and example.mp4 (489.6 MB)
    • 2. Recombining (Crossovering) Genes in Chromosomes Coding and example.srt (27.0 KB)
    • 3. Coding a function to crossover two parents.mp4 (379.7 MB)
    • 3. Coding a function to crossover two parents.srt (26.2 KB)
    • 3.1 crossover.m.m (1.6 KB)
    5. Mutation
    • 1. Mutating Genes in Chromosomes Theory.mp4 (154.5 MB)
    • 1. Mutating Genes in Chromosomes Theory.srt (13.3 KB)
    • 2. Coding a function to mutate genes.mp4 (194.2 MB)
    • 2. Coding a function to mutate genes.srt (11.1 KB)
    • 2.1 mutation.m.m (0.2 KB)
    6. Elitism
    • 1. Elitism Saving The Best Chromosomes.mp4 (192.0 MB)
    • 1. Elitism Saving The Best Chromosomes.srt (15.5 KB)
    • 2. Cosing an example of Elitism.mp4 (338.0 MB)
    • 2. Cosing an example of Elitism.srt (18.9 KB)
    • 3. Coding A Elitism Function.mp4 (118.5 MB)
    • 3. Coding A Elitism Function.srt (6.3 KB)
    • 3.1 elitism.m.m (0.6 KB)
    • 4. An improved elitism.mp4 (195.5 MB)
    • 4. An improved elitism.srt (12.0 KB)
    8. Continuous version of the Genetic Algorithm
    • 1. Continuous Genetic Algorithm theory.mp4 (512.8 MB)
    • 1. Continuous Genetic Algorithm theory.srt (35.1 KB)
    • 2. Exploration and Exploitation in Genetic Algorithms.mp4 (214.0 MB)
    • 2. Exploration and Exploitation in Genetic Algorithms.srt (12.3 KB)
    • 2.1 Exploration_Exploitation.zip.zip (1.1 KB)
    • 3. Coding A Continuous Genetic Algorithm.mp4 (550.5 MB)
    • 3. Coding A Continuous Genetic Algorithm.srt (31.8 KB)
    • 3.1 CGA.zip.zip (3.4 KB)
    • 4. Visualizing the convergence curve and search history of GA.mp4 (245.7 MB)
    • 4. Visualizing the convergence curve and search history of GA.srt (14.2 KB)
    • 5. Tuning the parameters of the Genetic Algorithms.mp4 (225.0 MB)
    • 5. Tuning the parameters of the Genetic Algorithms.srt (14.5 KB)
    9. Applications of the Genetic Algorithm
    • 1. Case Study 1 Solving Gear Train Problem.mp4 (313.1 MB)
    • 1. Case Study 1 Solving Gear Train Problem.srt (17.8 KB)
    • 1.1 GearTrain.zip.zip (3.7 KB)
    • 2. Case Study 2 Image Reconstruction.mp4 (600.3 MB)
    • 2. Case Study 2 Image Reconstruction.srt (37.1 KB)
    • 2.1 CaseStudy2.zip.zip (6.8 KB)
    10. Bonus Video
    • 1. Thank You.mp4 (35.3 MB)
    • 1. Thank You.srt (2.0 KB)
    • Visit Coursedrive.org.url (0.1 KB)
    • ReadMe.txt (0.2 KB)

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