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Did you know that you can simulate evolution inside the computer? And that you can solve really really hard problems this way? In this tutorial, we will look into the question: What are genetic algorithms? I will try to explain genetic algorithms using an example. And we will look at different applications of this evolutionary algorithm. We will also try to solve one historic problem in computer science by example: The Knapsack problem. Genetic algorithms are a subgroup of evolutionary algorithms or evolutionary computing and they are used in self-learning machine learning algorithms and AI. They use the concept of natural selection to simulate the survival of the fittest and natural selection inside your computer. This video is number one of a course of video tutorials to teach you the very basics of genetic algorithms in Python. 🙏 Support me: https://www.patreon.com/kiecodes 🛰 Join our Discord, to interact with other Coders and me: https://discord.gg/j7MXYeTAJd Check out my newest video: https://youtu.be/LW1i-axSoYE Follow me here: www.facebook.com/kiecodes www.instagram.com/kiecodes Questions of the day ■ What would you use genetic algorithms for? P-VERSUS-NP-PROBLEM: ■ I love the simple explanation and the relations to the Simpsons and Futurama: https://www.youtube.com/watch?v=dJUEkjxylBw RESEARCH: ■ Eiben, A. E. et al (1994) "Genetic algorithms with multi-parent recombination" ■ Geijtenbeek, van de Panne, van der Stappen (2013) "Flexible Muscle-Based Locomotion for Bipedal Creatures" - https://www.goatstream.com/research/papers/SA2013/index.html ■ Hornby, Globus (2006) "Automated Antenna Design with Evolutionary Algorithms" - https://ti.arc.nasa.gov/m/pub-archive/1244h/1244%20(Hornby).pdf 📚 SOME OF MY FAVORITE BOOKS: As an Amazon Associate I earn from qualifying purchases through the links below. ■ Ego is the Enemy by Ryan Holiday: https://amzn.to/3hQLjwJ ■ Deep Work by Cal: Newport: https://amzn.to/3hRyW3B ■ Atomic Habits by James Clear: https://amzn.to/3p0ODrW ■ Clean Code: https://amzn.to/3s1JMsn ■ The Clean Coder: https://amzn.to/38lKHft ■ Clean Architecture: https://amzn.to/394bcVU ■ The Pragmatic Programmer: https://amzn.to/2XfisJ2 ■ Test-Driven Development by Example: https://amzn.to/3bfNzfL ■ The Art of Computer Programming: https://amzn.to/3hQUzRJ ■ Design Patterns: https://amzn.to/2Ld09Sh ■ Refactoring: https://amzn.to/396NHLP ■ The Mythical Man-Month: https://amzn.to/3hPB1Ng ■ Working Effectively with Legacy Code: https://amzn.to/2XhOiF5 ■ Introduction to Algorithms: https://amzn.to/3bhWflQ ■ Extreme Programming Explained: https://amzn.to/3hS37Yc 🎥 MY GEAR: As an Amazon Associate I earn from qualifying purchases through the links below. ■ Coding Headphones: https://amzn.to/3s2gYAc ■ Mobile Headphones: https://amzn.to/2Xfhs7K ■ Monitor: https://amzn.to/2JPrVnc ■ Keyboard: https://amzn.to/396MG6t ■ Trackpad: https://amzn.to/3olgrqA ■ Camera: https://amzn.to/3s0R13R ■ LED Lights: https://amzn.to/39a1r8x ■ Tripod: https://amzn.to/35hoNYG ■ Speaker: https://amzn.to/395x97a ■ Studio Headphones for Editing: https://amzn.to/2LcgtCX ■ Audio Interface: https://amzn.to/2JRAC0l Timestamps: 00:00 Intro 00:23 The Problem 02:48 The Knapsack Problem 03:20 What are Genetic Algorithms 04:17 How does it work? 06:49 Summary 07:52 Is it worth it? 08:40 Results 10:23 Applications --- This video contains advertising content. --- Attribution: ■ Photo by Anush Gorak from Pexels - https://www.pexels.com/photo/man-holding-barbell-1431282/ #python #machinelearning #geneticalgorithms
