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.
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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
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
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Attribution:
■ Photo by Anush Gorak from Pexels - https://www.pexels.com/photo/man-holding-barbell-1431282/
#python #machinelearning #geneticalgorithms