I'm so excited for this to continue. It's the most clear and applicable explanation of this topic that I've seen so far!

Man, I love watching this guy teach. He makes things pretty easy to understand. Heck, even listening to this guy talk is entertaining.

I once made a project with a certain population of entities in a closed space, and they all had a random value for "beauty", on a scale of one to ten. As they wandered around and bumped into each other, they would decide if they wanted to mate. But each entity only said "Yes!" if the potential partner was their own beauty level or better. The child they had was given a value for beauty based on one of the parents for heredity. Eventually, the entities grew old and died off. I also kept a running tally of the average beauty of the population over all. After running the simulation for a while, the average beauty slowly went up until everybody was absolutely gorgeous. 10 / 10. And there wasn't even a single 9 left anywhere. It made me wonder why this hasn't happened in real life human populations. And then it occurred to me, "Oh yeah. Alcohol."

I am SO happy to find a video that isn't python. Thank you from the OOP community.

Great to see someone teaching coding with such passion, love your work!

Amazing content, surely the clearest tutorial for genetic algorithms!

Okay. On the first video I thought that this guy was crazy or something... But hey, if you sticked to the video, you saw that this guy has a very special way to communicate and teach. Talking about Genetic Algortihm, his way of teaching it's very easy to comprehend and it's well explained. Congrats. Don't stop making videos and teaching with that awesome energy and thanks for the lessons ! Hope to see more videos like this in youtube. Got here by Jabrils.

He deserves more subscribers to be honest. I learned so many things from his book and KZfaq channel.

Finally, finally, finally, got the video to understand Genetic Algorithm in simple words✌️

Hard problems explained in a simple way. You are a pure genius. Your energy is contagious. Keep going...

This man deserves way more subscribers or needs to be the next elon musk or something

This guy is my spirit animal.

"I love CS. This is just not a CS channel. It's something else. I don't know what it is." Lol I love him :3

You are the 2nd best teacher ever. Your explanations are so clear and precise. I love learning from you, but not as much as from Hank and John Green.

Amazing work, you made this algorithm sound so easy and understandable. Subbed. Keep up the good work! :)

Amazing!! I'm am a mechanical engineer looking to use GA for optimization of heat exchangers and these videos helped me understand the basics of GA. Moreover listening to your lecture is fun. His non stereotypical way of teaching is fun!!

Really this was so informative, and fluently and easily explained. The best part is getting examples and generating them on the go. This provides a really clear picture of the topic.

I'm in Bioinformatics, I just implemented an GA for metabolic data

i could not find a better explanation regarding this topic, THANK YOU

Why can you not be my teacher for literally everything?

You're so funny and at the same time informative. Kudos!

Damn, you're a great teacher

I've never learned this topic before. It's so exciting! I wonder how machine learning relates to this if at all. They sound a little bit similar to me.

...this was the first video I watched while taking notes.

these series are so entertaining to watch

Please do more of the theory part before coding, no hesitations. I play your theory videos and loop them, sleep. I always wake up as a genius.

Such amazing content delivery makes me wanna cry. All the very best with your future endeavors sir.

maaan, thanks for making it sound as simple as possible! subscribed!

I just watched this show on HBO called West World. The guy introduces small mistakes into the code. First thing I thought of was the guy was using this genetic algorithm to help his robots learn. They didn't say for sure but it's gotta be this. I just thought it was neat to see Mr Shiffman is ahead of HBO! :P

My complaint to you is that I haven't found you earlier! Purple rain was the first one for me, No I've been watching your playlist in sucking in information, actually so much that I had nightmares one night about you define ellipse!! I love that you are really impulsive and bubbly in your videos! great entertainment!

4:51 "Stuffed animals. Weird. Yeah. Ok. But anyways." LOL. Man you crack me up sometimes.

Nice work mate. Trying to follow your approach, but implementing it in C, just to inject a bit of pain into my life.

You are the mensch, my Friend. Thank you!

Wonderful presentation, finally I can understand something about this.

Hello, very cool video congratulations !!! One question I have is: If I do not know the result that my generations should have, will this algorithm not work? For example, am I required to tell what result the algorithm should arrive with the generations created? As in the example quoted the phrase "to be or not to be", I am specifying that the result I want is this, but what if I don't know that this is the result the algorithm should get?

This looks pretty cool, I will definitely try to create my own natural selection functions, thanks for sharing daniel

Not as if it made any difference, but at around 12:04 the fitness for "pancake" is actually either 0 or 2 depending on whether you take character position into account or not. If you do, then "n" is in the wrong position, if you don't, then "c" is a match too. Great material, though.

great work daniel a big thumbs up!!!!!!!

Why you only have 963 Like. You explained much easier than my teacher .....

i love this channel

Great Explanation!!!

After watching this I decided to try to code the algorithm without "spoilers" from Dan's actual code video. I made it in Python using pygame for the interface and surprisingly the program ran succuessfully (!) with no single error at the first try. I decided to implement various crossover methods and what I observed is that using the following "half-half" crossover: def crossover_half(parentA, parentB): res = parentA[:math.ceil(len(parentA)/2)] + parentB[math.ceil(len(parentA)/2):] return res I got the result for "to be or not to be" in about 7 to 12 seconds. However, using the following "random" crossover: def crossover_random(parentA, parentB): res = "" for i in range(len(parentA)): res += (parentA[i], parentB[i])[random.randrange(0, 2)] return res I achieved results always in less than 2 seconds. Also a very unexpected result was that using a "checker" kind of crossover: def crossover_checker(parentA, parentB): res = "" for i in range(len(parentA)): if not i%2: res += parentA[i] else: res += parentB[i] return res I *never achieved a result*. The population was almost uniform with bad solutions. The mutations had no major effect. All measures were made using a population size of 400 and a mutation rate of 1%.

good series you have explained it well. Keep up the good work

A complain in a comment.... nah! Your videos are awesome!

Thank you very much for the knowledge (from Philippines)

thalaiva ...u r great......

"Less Faster" -Daniel Shiffman 2016

The first time you've runt the car example the algorithm has found a good 2D car with three wheels which was going on forever. Perfect!

You are really good at explaining it :D

This person is amazing! Thanks for the video :)

thanks a lot for making this video and series

Thinking in terms of the analogy with Biology, is it correct to think that the phrase "to be or not to be that is the question" is the fittest "specimen" for this imaginary "environment" of phrases?. Besides, i see that in this example correlation is the nature of fitness in this imaginary world, meaning that the more similar any phrase is with "to be or not to be that..." the better it adapts to this environment and therefore the likely is for it to survive and pass its genes. This is very interesting because correlation is very present in our current technology, specially telecommunications, which make evolutionary algorithms a interesting tool to apply to this fields.

Thank you so much, so elaborate!

Love the energy thanks for the explanation:)

Awesome! Awesome! Been trying to apply neural evolution to brick breaker after watching your flappy bird series :-P

Hi Daniel! Great series of videos. They are helping me a lot. However, I think there's kind of a mistake at the crossover explanation. In the 'single-point' crossover that you explain, I thought that it produced two childs as a result, in order to have, in the next generation, N individuals again. Am I correct?

Loving it!

this is awsoooooooome ... please make more ...

Thank you for your effort.

Really nice and interesting video! Keep it up :)

this is something else... this is epic

Thank you for keeping it add free

gays all around the world need to know the existence of this man because he will properly introduce them to code

Love your energy! :)

What about entropy? Should the entropy change as the generations fly by?

Thanks you, but only creationnists algorithm should be teach at schools, praise the lord, amen

wow, great man! thanks a million

Nice video. Keep it up

How cool is that!!

Thanks man🙏

pancake has a fitness of 2 BTW!

For the breeding process, could you just make the incorrect letters be the one that mutate instead of the correct letters mutate?

This video made my mind happy as a Rainbow Unicorn.

I am confused!! Is fitness value just the number common characters present in both string....or the numbers of characters present on same corresponding locations in both strings? Which one is it?

Does checking of how many characters match the target really work? In that case niurnco can also match unicorn, yeah?

I can see how some elements of human cognition would use a kind of genetic algorithm.

1.5x is watching ADHD at its best :P (You rock dude. Thanks for making this)

Great. Now where do we add the Perlin noise?

How to define fitness function or fitness score if the ultimate goal 'unicorn' is not known to us?

MEGA! thanks man

good job bro, but we could only choose 2 parent every time? and how we can crossover our element whene we have 5 element(we croosover element one with element 2 and element 3 with 4 and element 5 with who???)????

"People do strange things with softwares"

U DA BEST

On boxcar2d, does the population consist of all of the created cars? And the DNA would be each car?

Hey dan, I was wondering, what is the use of this kind of neural network? We already know the sentence..

I don't have a clue at all of coding, algorithms and stuff like that, but I thought that maybe a sentence or a word isn't the best example, because to define the fitness in that example you have to know the solution, which makes the whole thing obsolete...

Can i get tips on how to apply genetic algorithm for a class scheduling system?

19:24 isn't it better to store where the characters match and choose two phrases that have a high fitness score but at the same time does not have matching characters in the same position? I'm a beginner so i really don't know but that seems more logical.

You are so awesome 😎

The real sheldon cooper :)

This is fantastic stuff! As a biology student considering studying evolution who got into computer programming, I couldn't imagine something im more into. Thanks! Maybe pop a xanax before next recording? ;)

Could someone explain to me how do we get N children? Do we pick only 2 parents and generate N children with crossovers, in which case how do we generate random crossovers. Or do we redo parent picking and apply the same crossover + mutation to get a new child with the risk of always picking the same parents. I feel I miss something here

How much weed Shiffman has to take before recording? Shiffman: yes

4:24 its the second time you mentioned rainbow during these tutorial series. Not sure where this is going..

Could you just let the algorithm change its mutation rate itself depending on how fast the fitness raises? Like "Fitness is raising slower and slower, let's just set my mutation rate to 5% instead of 1%"

Side tangent, Anyone noticed the hereditary sound effect at 3:35

This algorithm suggests that "evolution" has a telelogical process with some logos to an "end result" or purpose ... ;)

And what happens if you only have one object, i mean in the boxcar2D how do you crossover? by the way love your videos, big fan from Mexico!

'p a n c a k e' has a fitness of 2 . 'n' and 'c' !!!! I did the same mistake too initially xD