Genetic Algorithms Explained By Example

[@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?*

[@DevOpsDirective]

I first learned about evolutionary algorithms from this TED talk www.ted.com/talks/bill_gross_a_solar_energy_system_that_tracks_the_sun/ Since then I have wanted to use them to write a program that attempts to learn how to write brainf***. BF seems like a good language choice since there are only a handful of symbols and writing/instrumenting an interpreter to also evaluate an objective function to feed back into the evolutionary algorithm would be simple! I haven't gotten around to it yet, but perhaps soon!

[@Stopinvadingmyhardware]

Thanks, you just helped me solved a vector problem I was having. Oddly, I didn’t even use an algorithm.

[@rickyricardo75]

very interesting ! let us know if you applied this to trading !

[@kx9473]

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.

[@KieCodes]

You are more than welcome my friend. 🙏 Rock on! 🚀

[@Vaderir]

2 hour’s lecture in 12mins how about that .

[@Terra_Cognita]

Do you know how he calculated the total number of combinations in a naive brute force approach? I did permutations and combinations and the values were not equal.

[@Terra_Cognita]

Never mind I figured it out, It is the sum of all possible (N chooses k) combinations k = 0, 1,..N which simplifies to 2^N

[@mr.boomguy]

This is why I love watching KZbin. Sure, 95% of the time is trivia and entertainment, but then you stumble upon things you either where looking for or not, and you learn them in minutes instead of the years it takes from professional lecturing. (Granted, processing that information is a whole other ordeal, but at least you have it on hand)

[@xandrovich5976]

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.

[@KieCodes]

Thank you so much! 🙏

[@sergioseco9835]

this deserves a million views

[@KieCodes]

Thanks Sergio 🙏

[@michaelfekadu6116]

I agree

[@KieCodes]

Thank you. 🙏

[@matt-g-recovers]

not joking!

[@KieCodes]

@@matt-g-recovers Thanks as well! 😅

[@evrmk]

Three 1.5-hour-long lectures in my university explained in 11 minutes. Thanks!

[@abdullahsheriff_]

According to me, his video is THE BEST explanation on KZbin 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.

[@KieCodes]

You are more than welcome! Enjoy your studies!

[@adityagarg2185]

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?

[@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!

[@KieCodes]

You are more than welcome my friend. 🙏 Rock on! 🚀

[@willboler830]

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.

[@lohithkandukuri5579]

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

[@KieCodes]

Thank you very much! 🙏🙏🙏

[@subpixelsalt7648]

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.

[@KieCodes]

Thank you! I totally feel the same. It is modern magic. 🙃

[@BehniaFB]

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

[@KieCodes]

Thank you so much!

[@zacrobyte]

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

[@KieCodes]

Thank you. 🙏 Maybe some day. Thanks again for the motivating words.

[@kritagyagupta8619]

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

[@KieCodes]

Thank you. 🙏 I am glad I could help. And thanks for your nice words. ☺️

[@CristalMediumBlue]

Toll. Das ist das beste Video, das ich bisher zu diesem Thema gesehen habe

[@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!❤)

[@colinmignot6309]

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

[@KieCodes]

Thank you. 🙏

[@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.

[@KieCodes]

Thanks so much for your kind words. 🙏

[@MeANDmyPETgoat]

Great video bro, helped me understand what my teacher's mumbling couldn't for our test, keep it up!

[@KieCodes]

Thanks for your kind words. 🙏

[@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!

[@KieCodes]

Wow. Thanks for your kind and motivating words Mo. I will try my best. To be honest I wouldn’t be ready for millions of subs. Glad to hopefully hit a 1000 till 2021. Have a great day! 🙏

[@KieCodes]

And thanks for subscribing! One sub closer. ☺️

[@MaTrOoOoD]

@@KieCodes dude your videos are extraordinary! I couldn’t resist 🤷🏻‍♂️ Wish you best of luck man! Can’t tell you how happy and excited I am to see what you have next!

[@aliticx]

Man I wish everyone explained concepts this good! Awesome video!

[@KieCodes]

Thank you Aliticx. This comment really means a lot to me. 🙏 Having people to really understand what I try to explain is way more worth to me than subscribers and views. Thank you.

[@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

[@KieCodes]

Thanks George. This means a lot and is really motivating! Great to have you here. Rock on!

[@SHUBHAMJHA-o3g]

This is the hands down the best video on genetic algorithms!

[@BraidyMerkle]

I have not yet had to use genetic algorithms, but want to try them out after watching this video! Thanks for the introduction!

[@KieCodes]

Thanks Braidy! Let me know what you used them for. :)

[@kr0tyara]

You are such an underrated gem. GOOD JOB!!!

[@KieCodes]

Thank you. 🙏

[@vasachisenjubean5944]

man the effort you put in this video is - "Salute"

[@KieCodes]

Thank you. 🙏

[@seriouscoder1727]

I,ve watched many clips in this regard but your explanation was one of a kind, absoulotlly deserve thousends of million of billion views

[@KieCodes]

Wow. Thank you for your kind words! 🙏

[@srinivaskollipara1293]

this is awesome!!!!!!! I am a mechanical engineer with 0 knowledge in these type of algorithms but for the sake of a project on scheduling using the genetic algorithm I was surfing youtube, this is the best explanation I ever had and it is very clear if possible, please suggest algorithms similar to this which are good for job shop scheduling.

[@Joselo09111]

Fantastic explanation! I learned more in this video, than my professor

[@KieCodes]

Great to hear! Thank you so much!

[@marflage]

I can't thank you enough. Tomorrow's my AI exam and your video helped me prepare for it. I wish your channel explodes with magnitudes of subs. Thank you so much!

[@KieCodes]

You are more than welcome my friend. 🙏 Rock on! 🚀 Good luck with your exam!

[@marflage]

@@KieCodes thanks!

[@taylan8973]

I rarely leave a comment but your explanation is just amazing. Good job

[@KieCodes]

Thanks Taylan! 🙏

[@chaosrider100]

All is fine and useful from this video and I will recommend it to my students as a quick introduction, BUT this is not natural selection - it is called "directed" or "purposeful" selection. It is similar to what you have in ML for supervised learning - there is an aim (manifested by the elitist genomes)... Natural selection works aimlessly and randomly. The biggest difference between the two is the speed of convergence - as Natural selection is a notoriously slow process.

[@KieCodes]

Thank you for the clarification! 🙏

[@thop7964]

Fantastic example. Really helps show how the conceptual steps actually work!

[@KieCodes]

Thank you. 🙏

[@1stpatrickha]

To be honest, this is the best video explained about this Genetic Algor concept. Quality contents. Keep going dude!

[@KieCodes]

Thank you. 🙏

[@mesticasamelapapna708]

Thank you for this Kie! I'm amazed that you use Genetic Algorithm to solve the Knapsack problem :)

[@samidahdouh7863]

This is actually one of the best videos I've ever seen on KZbin. Thank you!

[@KieCodes]

Wow. Thank you. 🙏🎉

[@marcellorossetticonti3002]

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.

[@mgwyndolin4262]

Hi thanks for sharing your insight! It is very instructive and helps me clarify the confusion about the top performers of the previous generation being mutated instead of kept intact in the video. But this video really offers a great explanation.

[@paragvadhiya8019]

Video is on point, accurate and does not waste your time. Thanks so much man.

[@KieCodes]

I really appreciate your kind words! 🙏

[@arianalini8636]

how is it not booming yet?! thank u for the clear explanation + the awesome edits!

[@r.ramirez1521]

Please don't ever go away. This was amazing

[@KieCodes]

Thank you. 🙏

[@Copybook]

I was really hard to digest it and I don't mean mathematically BTW, tomorrow I have an exam and you save my life, a blessing from an AI student

[@DerClaudius]

Good overview. I would suggest a fitness function that goes negative for non-solutions, so even if you have no solution, because the problem is so hard, you still keep the genomes that are closest to a real solution. With having fitness=0 for all non-solutions, you can't distinguish how bad or close the genomes are.

[@KieCodes]

Good idea! Thank you. 🙏

[@sardororipov1302]

I’m here before this channel get 1million subs. Keep up the great work man!

[@KieCodes]

Thank you. 🙏

[@diegocruz8187]

This channel should have more subscribers! Really amazing video and explanation

[@KieCodes]

Thanks Diego! 💪🙏

[@fasaipuengudom5412]

Best video regarding this topic. Very visual and easy to understand

[@TheTehnigga]

Really well explained and cured, these are the kind of explanations that I prefer, there's love and dedication behind it. Thank you!

[@KieCodes]

Thank you. 🙏

[@mahdilatreche4501]

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

[@KieCodes]

Thank you. 🙏

[@annashchukina3768]

This was an amazing introduction to the topic and great intuition. Thanks for the video! It was also fun to watch😄

[@KieCodes]

Thank you. 🙏

[@Mohamed_Shabaik]

I don't usually make comments. but that was a really well-explained and interesting video, Thanks!

[@zecool7041]

really good explanation , better than most of videos that i have seen and better than my teacher's lecture , keep up the good work , greetings from algeria

[@KieCodes]

Thank you for your kind words! Have a great day. 🙏

[@daphnelenders4207]

Thanks a lot for your explanation, looking forward to use genetic algorithms in the future!

[@KieCodes]

Thank you. Have fun, its a great tool!

[@daesk]

My teacher used 2 hours of class to explain what this man explained in 10 minutes, thanks

[@KieCodes]

Thank you 🙏! Good luck for your studies. Take care.

[@pedrocamunas5625]

I can see english is not your first language but still your pronunciation is really clear, thumbs up

[@KieCodes]

Thank you

[@nishitpatel5686]

one of the best videos on genetic algorithm

[@KieCodes]

Thank you. 🙏

[@hansergonzalez6646]

What an amazing content, explained with simple words. Thanks and keep up the hard work.

[@KieCodes]

Glad you liked it! Thank you!

[@HarunFarah]

Great vid done a better job at explaining than a university lecturer

[@KieCodes]

Thank you very much!

[@ForTheOmnissiah]

Wish you uploaded more. This is quality content.

[@GlynisDance]

That was an excellent video. Clear, engaging, inspiring and fun! I will be watching more of your videos.

[@KieCodes]

Thank you!

[@AK-gx4fh]

Yey! now my mom can really know what i do for a living :)

[@avixx]

You showed this in a way easy to understand! Thanks!

[@adinvella4665]

Very informative video; full of information yet very easy to follow. Thank you!

[@TWFDeadzoneII]

Mind blowing. I can't wait to implement this.

[@KieCodes]

It surely is. Have fun!

[@rajathalexander406]

best possible explanation, became ur FAN

[@KieCodes]

Thank you and welcome! 🙏

[@sevdalink6676]

Whoah men! You fascinated me. What a level of explanation.

[@KieCodes]

Thank you. I hope it helped. 🙏🫡

[@alvaroarcelus8949]

Amazingly explained!! Thank you so much. 🙌

[@shouryaraj5913]

Astonishing video! Thanks for sharing the incredible research papers. Looking forward to learn and explore more. Thanks

[@KieCodes]

Thanks for you kind words! :)

[@aaradhyanegi639]

I've never learned anything in this way I love it!

[@KieCodes]

I am glad to hear that!

[@Abdul-RafayHamid]

Really well explained. I will appreciate it if you can make a project to solve time table scheduling problem with genetic algorithm.

[@wahalaq36]

You did a great job with this, two years ago i searched heaven and earth but didnt find a thing

[@KieCodes]

Thanks a lot! Glad I could help.

[@junbongzz0523]

I recommend this video to those who wants to simply know what GA is in 10min!!

[@KieCodes]

Thank you. 🙏

[@ragabnadim]

Thank you for the video, will recommand to my students

[@АндрейВикторович-т1л]

You're awesome! I don't speak English as well, but your explanation is really good, and I understand the main idea!

[@KieCodes]

Thank you. 🙏

[@SuperNightmareID]

That booping noise had me checking my pc and phone every time lmao.

[@KieCodes]

😅 sorry

[@johntravolta2655]

Greeting from Polish college. Wish you the best :)

[@KieCodes]

Thank you! To you too.

[@hoaralatoan845]

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

[@KieCodes]

Hey. Thank you. The knabsack problem.

[@yasminejas5007]

Thank you so much this video is very helpful for my thesis!

[@KieCodes]

Glad it was helpful! 🙏

[@muhammadqaisarali]

Start with a Problem presentation is a great way to learn.

[@KieCodes]

Thank you. 🙏

[@muhammadqaisarali]

@@KieCodes welcome sir, it is a great video... Again heartiest thanks...

[@KieCodes]

You are more than welcome my friend. 🙏 Rock on! 🚀

[@SintaxErorr]

*The Best Solution* [Phone, Headphones,Baseball Cap,Laptop,Coffee Mug] Weight: 2980 grams *My Genetic Algorithm* [Phone,Headphones,Mints,Notepad,Tissues,Laptop] Weight: 2998 grams. ooop made one before watching your other video to test myself so not sure if you found this in that video. but this video is great

[@KieCodes]

Thanks for your kind words! 🙏 and great effort implementing your own GA before watching my video. Amazing 💪

[@michellesainos3443]

Thank you!!! your explanation was very clear and awesome!! Definitely checking out the implementation

[@KieCodes]

Thank you Michelle!🙏

[@ouahidalalga9437]

Just Wow! Hats off to you, that's a great explanation.

[@KieCodes]

Thanks a lot!

[@mcbot1344]

Wow this is incredibly helpful for my exam. Keep up the good work! Greetings from Germany

[@KieCodes]

Thank you for your kind words, Mc Bot. 🙏

[@padmakarrao4681]

"at some number of time, you won't just miss your flight, but the whole solar system will be gone" 🤣🤣🤣 I love this video...

[@KieCodes]

Glad you liked it! 🙏

[@ammarazlan2919]

Thank you so much! Please do Clonal Selection Algorithm next please...!

[@roct07]

This is so well explained

[@yelamankarassay]

Great You helped me in my HW assignment.

[@KieCodes]

You‘re welcome.

[@LonleyGuns]

I like the way you explain algorithms.

[@KieCodes]

Thank you for your kind words, LonleyGuns. 🙏 I put a lot of work into it. So that means a lot. What other algorithms are you interested in?

[@LonleyGuns]

@@KieCodes Pattern search like Hooke Jeeves would be great. Also, new programmers face problem which approach to use: objective function or array as input values into optimisation problem. Would be great to hear some advices when to use which method.

[@mikolajwojnicki2169]

You seem to stress the importance of the crossover function, but for my problem I don't think it's doable because I need to always keep a constant number of 1's and 0's. I hope just using mutations in which two digits switch places is good enough.

[@70ME3E]

yeah he left that part out where if you have an invariant, like in your problem that you have to keep, then there is a way genetic algos deal with that to restore the invariant after crossover. I think it should be a custom callback you can have the algo run when it's needed. the whole thing would not even work at all without crossover

[@centarproduction7667]

Great content! I'll subscribe now so I can later be salty in the comments how I was here when you only had 1320 subs :D

[@KieCodes]

Thanks for being here centar! 🙏 I am looking forward to reading your salty comments later. 😅

[@TheCzarsoham]

Great video! Exactly what I was looking for!

[@KieCodes]

I am sooo glad it helped you! 🙏

[@MohamedHassan-xf5wb]

Really well explained

[@jiahaochang8628]

This is an awesome video to match. Thank you for your explanation, that is a great visual approach!

[@KieCodes]

Thank you! 🙏

[@o.rudyemonvuon64]

Absolutely amazing !!!! You perfectly outlined the points and I'm defenetly more intrigued in doing additional research and watching your other videos !!!

[@KieCodes]

Thank you for your kind words.

[@johnostensen]

"At some number of items, you wont only miss your flight, but the whole solar system would be gone" 😂

[@catatanbethari1226]

Bro... You help me with my thesis... Thank you very very muuuch...

[@KieCodes]

You are more than welcome, Catatan! 💪

[@mohammadabdurroufhrithik5152]

very helpful video!!! excellently explained!

[@husnasyed7388]

Is it possible for you to elaborate how to generate a fitness function for the problem you are trying to solve? That is where I find myself stuck

[@KieCodes]

Sure. In this problem I want to have the most value in my backpack whithout over-reaching it's capacity. So for each genome, that defines a set of items inside my backpack, I add all the weight. If the weight is already heavier than I can carry in my backpack, then the solution is not valid and its fitness is 0. If the weight of the set of items fits inside the backpack, I add up all the values of the different items and the accumulated value of all items is the fitness. So a genome with a solutions that provides more value is fitter than a solution that provides less. In my follow up video you will see the code explained and a link to a github repository in the description. I hope that helps. Happy coding!

[@husnasyed7388]

@@KieCodes so that means the mathematical function is that the sum of the weights must be equal to or less than 3 kg? I think I am struggling more because the last time I used C++ was 10 years ago! Alright now next question is that in your video each item is assigned a binary digit either 1 or 0. How do you decide which is 1 and which is 0? Sorry if that's a stupid question.

[@husnasyed7388]

@@KieCodes will check out your next video ! Thank you for being one of the people that help others without even knowing it.

[@KieCodes]

The binary representation is to encode a solution. 0 means not inside the backpack and 1 means it is inside. These genomes are generated randomly. After that they are evaluated (fitness function) and recombined (crossover function) and slightly changed (mutation function) and thus way we get a new set of solutions/genomes. It is an algorithm based on the idea of evolution and it surprisingly works to get better and better solutions with every step of the process. I hope that clears it up a little but more.

[@husnasyed7388]

@@KieCodes yesss got it!! Thanks a lot!

[@hzmuhabbet]

Great explanation dude, thanks so much.

[@KieCodes]

You are more than welcome my friend. 🙏 Rock on! 🚀

[@sallygoldin5558]

Wonderful, clear and engaging!

[@shahed.hijjawi]

GREAT EFFORT BRO!

[@harishsingh5225]

Explained brilliantly.

[@KieCodes]

Thank you so much. 🙏