Genetic Algorithms Explained By Example

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:

RESEARCH:
■ Eiben, A. E. et al (1994) "Genetic algorithms with multi-parent recombination"
■ Hornby, Globus (2006) "Automated Antenna Design with Evolutionary

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:

#python #machinelearning #geneticalgorithms

[KieCodes]

Thanks for stopping by! Today we will encounter a problem we can't easily solve with our normal algorithmic tools so we deep dive into a class of algorithms called: GENETIC ALGORITHMS!
I hope you find it as interesting as I do. *What would you use them for?*

[kx]

This guy explained everything from 2 weeks long boring lecture in under 12 minutes with extremely clear explanation. I love you because you just saved my degree.

[xandrovich]

I must say - the quality of this video is top-notch. The crisp editing, sound, and illustrations do a great job of demonstrating the point.

[adityagarg]

This video was AMAZING. I can't believe the amount of time and effort you took to explain this topic so clearly. It sucks that you stopped making videos. What can I do to motivate you to make an algorithm series?

[willboler]

Excellent video! One of the better selection algorithms and more typical is a tournament selection. Rather than choosing the best fitness genes for the next generation, each gene is given a probability of selection based on their fitness. Better genes have a higher probability of selection, and worse genes have a lower probability of selection, but the odds of selection are not 100% and 0% respectively. This takes a bit longer when it comes to finding a solution, but it gives a better search of the solution space.

[TarunRavi]

Wow your video is awesome! You explained a pretty complex algorithm in a simple way. I've always wanted to use genetic algorithms for one of my videos, this is a fantastic intro to the field! btw I love your video quality, it looks soo good.

[abdullahsheriff_]

According to me, his video is THE BEST explanation on YouTube on Genetic Algorithms. My mates and I understood Genetic Algorithms like never before, loved it, and implemented it in our University Course Project.

Thank you, Kie.

[BehniaFB]

I've never imagined that one day Seth Rogen will be teaching me Algorithms😂 (Jokes aside this video was amazing and well-made; GOOD JOB!❤)

[georgeruellan]

Dude this video is amazing. Great explanation, I liked the real world example too. Your editing style is sick too! Can't wait to see more from you

[zacrobyte]

Yo dude, this is some next level way of teaching your channel should've had atleast 1 mil already

[colinmignot]

you're great teacher. taking time to edit engaging videos with visuals like you do is very effective to help ppl grasp concepts

[subpixelsalt]

Great video! I've been facinated by genetic algorithms after seeing people use it to beat levels in Super Mario Bros. There's just something so fascinating about watching a computer "learn" how to get better at doing something.

[eddazzo]

Im a computer science degree student and this helps me to understand better on my final year project's topic! Thank you very much sir!

[kritagyagupta]

Dude, Your videos are higher quality then Pewdiepie and Dream combined
Not exaggerated
I have literally understood a concept u have tried to learn from past 10 months

[MaTrOoOoD]

After watching the video I opened the channel expecting to see millions of subscribers. You deserve much more!! What an awesome video. Simple and right to the main points! Keep up the good work!

[marcellorossetticonti]

Hi Kie, congratulations on the clarity of your explanation. It gave me some suggestions on how to improve the way I explain these algorithms.

I work in Italy as a physics researcher and in my group we routinely employ these kinds of algorithms to optimize the performance of particle accelerators redesgning them (with a similar approach to the antenna problem you mentioned). I can confirm that they work wonders at solving systems whose dynamics are extremely complex (especially when due to highly correlated variables)!

I wanted to tell you that in our code we apply elitism as the last thing (even following mutation) to be sure that the best fitness value never goes down in subsequent generations. If you apply mutation later, you risk losing the best solution from the previous cycle.
Nothing catastrophic though, the genetic information to reconstruct the best solution is mixed in the rest of the population anyway and will probably re-emerge soon anyway.

[hoaralatoan]

What a fantastic explanation! Clearly explain GA. Can you tell me what classical problem inspired you about the "backbag problem" in 0:23?

[BehniaFB]

Every second I that goes, I realize more how complete this video is ! This vid should definitely have more than a million views .

[lohithkandukuri]

gooood.... Nobody could have been more concise and informative at the same time.

[mahdilatreche]

This video is astonishing, you deserve a lot more views, keep up the good work :)