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

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English

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7.29 GB

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8da9a51453d87918171be6103a5d000601bc227d

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coursedrive

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14-05-2020 11:42 (edited 14-05-2020 12:12) by coursedrive

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File	Size
Introduction to Genetic Algorithms - Theory and Applications/1. Inspiration/1. Inspiration.mp4	208.46 MB
Introduction to Genetic Algorithms - Theory and Applications/1. Inspiration/1. Inspiration.srt	21.41 kB
Introduction to Genetic Algorithms - Theory and Applications/10. Bonus Video/1. Thank You.mp4	35.25 MB
Introduction to Genetic Algorithms - Theory and Applications/10. Bonus Video/1. Thank You.srt	1.96 kB
Introduction to Genetic Algorithms - Theory and Applications/2. Gene Representation and Fitness Function/1. Gene Representation and Fitness Function Theory.mp4	193.68 MB
Introduction to Genetic Algorithms - Theory and Applications/2. Gene Representation and Fitness Function/1. Gene Representation and Fitness Function Theory.srt	20.25 kB
Introduction to Genetic Algorithms - Theory and Applications/2. Gene Representation and Fitness Function/2. Gene Representation and Fitness Function Coding.mp4	383.84 MB
Introduction to Genetic Algorithms - Theory and Applications/2. Gene Representation and Fitness Function/2. Gene Representation and Fitness Function Coding.srt	20.72 kB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/1. Selection Using Roulette Wheel Theory.mp4	116.76 MB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/1. Selection Using Roulette Wheel Theory.srt	21.51 kB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/2. Selection coding an example.mp4	93.79 MB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/2. Selection coding an example.srt	19.11 kB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/3. Selection coding a roulette wheel function.mp4	182.15 MB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/3. Selection coding a roulette wheel function.srt	31.19 kB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/3.1 selection.m.m	1.24 kB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/4. Fitness Scaling in The Roulette Wheel.mp4	366.46 MB
Introduction to Genetic Algorithms - Theory and Applications/3. Selection/4. Fitness Scaling in The Roulette Wheel.srt	22.70 kB
Introduction to Genetic Algorithms - Theory and Applications/4. Recombination (crossover)/1. Recombining (Crossovering) Chromosome Theory.mp4	175.28 MB
Introduction to Genetic Algorithms - Theory and Applications/4. Recombination (crossover)/1. Recombining (Crossovering) Chromosome Theory.srt	18.40 kB
Introduction to Genetic Algorithms - Theory and Applications/4. Recombination (crossover)/2. Recombining (Crossovering) Genes in Chromosomes Coding and example.mp4	489.56 MB
Introduction to Genetic Algorithms - Theory and Applications/4. Recombination (crossover)/2. Recombining (Crossovering) Genes in Chromosomes Coding and example.srt	27.03 kB
Introduction to Genetic Algorithms - Theory and Applications/4. Recombination (crossover)/3. Coding a function to crossover two parents.mp4	379.70 MB
Introduction to Genetic Algorithms - Theory and Applications/4. Recombination (crossover)/3. Coding a function to crossover two parents.srt	26.22 kB
Introduction to Genetic Algorithms - Theory and Applications/4. Recombination (crossover)/3.1 crossover.m.m	1.63 kB
Introduction to Genetic Algorithms - Theory and Applications/5. Mutation/1. Mutating Genes in Chromosomes Theory.mp4	154.53 MB
Introduction to Genetic Algorithms - Theory and Applications/5. Mutation/1. Mutating Genes in Chromosomes Theory.srt	13.27 kB
Introduction to Genetic Algorithms - Theory and Applications/5. Mutation/2. Coding a function to mutate genes.mp4	194.21 MB
Introduction to Genetic Algorithms - Theory and Applications/5. Mutation/2. Coding a function to mutate genes.srt	11.10 kB
Introduction to Genetic Algorithms - Theory and Applications/5. Mutation/2.1 mutation.m.m	188.00 B
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/1. Elitism Saving The Best Chromosomes.mp4	192.03 MB
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/1. Elitism Saving The Best Chromosomes.srt	15.51 kB
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/2. Cosing an example of Elitism.mp4	338.03 MB
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/2. Cosing an example of Elitism.srt	18.88 kB
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/3. Coding A Elitism Function.mp4	118.52 MB
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/3. Coding A Elitism Function.srt	6.25 kB
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/3.1 elitism.m.m	653.00 B
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/4. An improved elitism.mp4	195.48 MB
Introduction to Genetic Algorithms - Theory and Applications/6. Elitism/4. An improved elitism.srt	12.03 kB
Introduction to Genetic Algorithms - Theory and Applications/7. Implementation of the Genetic Algorithm/1. Coding Genetic Algorithm in Matlab.mp4	747.32 MB
Introduction to Genetic Algorithms - Theory and Applications/7. Implementation of the Genetic Algorithm/1. Coding Genetic Algorithm in Matlab.srt	42.78 kB
Introduction to Genetic Algorithms - Theory and Applications/7. Implementation of the Genetic Algorithm/1.1 GA_example.zip.zip	3.02 kB
Introduction to Genetic Algorithms - Theory and Applications/7. Implementation of the Genetic Algorithm/2. Developing a GA function in Matlab.mp4	242.83 MB
Introduction to Genetic Algorithms - Theory and Applications/7. Implementation of the Genetic Algorithm/2. Developing a GA function in Matlab.srt	14.22 kB
Introduction to Genetic Algorithms - Theory and Applications/7. Implementation of the Genetic Algorithm/2.1 GA_function.zip.zip	3.02 kB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/1. Continuous Genetic Algorithm theory.mp4	512.75 MB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/1. Continuous Genetic Algorithm theory.srt	35.12 kB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/2. Exploration and Exploitation in Genetic Algorithms.mp4	213.98 MB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/2. Exploration and Exploitation in Genetic Algorithms.srt	12.31 kB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/2.1 Exploration_Exploitation.zip.zip	1.08 kB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/3. Coding A Continuous Genetic Algorithm.mp4	550.51 MB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/3. Coding A Continuous Genetic Algorithm.srt	31.83 kB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/3.1 CGA.zip.zip	3.36 kB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/4. Visualizing the convergence curve and search history of GA.mp4	245.69 MB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/4. Visualizing the convergence curve and search history of GA.srt	14.20 kB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/5. Tuning the parameters of the Genetic Algorithms.mp4	224.96 MB
Introduction to Genetic Algorithms - Theory and Applications/8. Continuous version of the Genetic Algorithm/5. Tuning the parameters of the Genetic Algorithms.srt	14.55 kB
Introduction to Genetic Algorithms - Theory and Applications/9. Applications of the Genetic Algorithm/1. Case Study 1 Solving Gear Train Problem.mp4	313.15 MB
Introduction to Genetic Algorithms - Theory and Applications/9. Applications of the Genetic Algorithm/1. Case Study 1 Solving Gear Train Problem.srt	17.82 kB
Introduction to Genetic Algorithms - Theory and Applications/9. Applications of the Genetic Algorithm/1.1 GearTrain.zip.zip	3.66 kB
Introduction to Genetic Algorithms - Theory and Applications/9. Applications of the Genetic Algorithm/2. Case Study 2 Image Reconstruction.mp4	600.28 MB
Introduction to Genetic Algorithms - Theory and Applications/9. Applications of the Genetic Algorithm/2. Case Study 2 Image Reconstruction.srt	37.11 kB
Introduction to Genetic Algorithms - Theory and Applications/9. Applications of the Genetic Algorithm/2.1 CaseStudy2.zip.zip	6.78 kB
Introduction to Genetic Algorithms - Theory and Applications/ReadMe.txt	220.00 B
Introduction to Genetic Algorithms - Theory and Applications/Visit Coursedrive.org.url	124.00 B
ReadMe.txt	220.00 B
Visit Coursedrive.org.url	124.00 B

Description

⚡️⚡️For More Udemy Courses Visit 👉🏻 https://coursedrive.org

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