Genetic Algorithm from Scratch in Python (tutorial with code) 

I have learned a *ton* from this video, but the biggest thing I've learned is about good practices with type hinting and software architecture. The way this is laid out is brilliant. If this is how everyone in the software industry codes, I am way behind, however I've never seen anyone else do it this way in formal courses or other videos. I first watched this months ago and I genuinely believe I have vastly improved as a programmer as a direct result. You should make more videos... about just anything. I guarantee people will learn a ton, regardless of what you're actually trying to teach

Yoooo this video is SO well made! As well as the first part, just great explanations, visuals/animations, and a clear rundown of the code you're writing. SO GOOD!

Hey, cool video. In 5:16 you can also use "else 1 - genome[index]". This will also flip the bit (or just xor it with 1).

Editing skills on point !!! Referring to the previous videos totally makes sense and makes the video more interactive.

wow. glad I ran in to you. you're videos are great! fast, to the point, informative, and a wee bit funny. sub earned, for sho!

Dude your content is really great and super high-quality and very easy to follow I 100% feel like you should definitely have more a following and I know that pretty soon you're gonna blow up, glad I'm here to see it

I'm so glad I found your channel. I can't believe you don't have more views- your production quality is fantastic and it is apparent you are putting a lot of time into each video. Keep up the great work!

Disappointed to see you haven't made other videos on similar topics! I loved both this and the first video on GA. Would be super interested in topics such as Simulated Annealing, Tabu Search and Evolutionary Strategies !

Just started my GA classes and this video helped me a lot! It was short, concise and very clear. Thank you for the content ♥

Thanks for the video! I recently found about the genetic algorithm and this video helped me understand it better. An alternative to that parte of abs(genome[ idx ]-1) is ( genome[ idx ]+1)%2.

So well done! Clean, clear and teaches a lot (both for the GA and python code/style). Nailed it! Thank you for the content.

too good my man, i have seen other youtubers use genetic algorithms, and after like a year i can finnally make my own TYSM

I am very grateful, both of your videos about genetic algorithms really helped me to approve my exam. Keep it up!

Good video, but this goes way too fast. This is supposed to be a 20-30 minute video, but you sped it and talk way too fast. I have been programming in Python for about 3 years, and I learned more about the nitty gritty type hinting functions and Callables than I did about genetic algorithms. Not to nitpick, but since the topic is genetic algorithms, it'd be best if you didn't use all these nitty gritty details from Python. Nonetheless, good video. But just letting you know I had to watch this video twice in 0.75x speed to understand what you were doing.

Nice. This problem solving algorithm will come in handy for many problems to come :) Thanks for the vid!

Thank you!! I had an assignment related to genetic algorithms and this is the best example I have seen for begginers:)

Fantastic introductory tutorial on this topic, thanks for posting this video. Any recommendations on books to start on genetic algorithms or genetic algorithm with Python? Thanks

Yeah nice high quality production! You got my respect!

hey this was really good! im gonna check out your other stuff.

You're a literal life saver! I'll be citing your videos on my uni project!

i really like the way you code in python !

It was really really helpful. I salute. I have no words.

Very good presentation!

A new subscriber. 🤗 Thanks for your explanatory teachings.

Kie, i think some uf us would need a tutorial on Callables after this video :D

Respect++ for maintaining good coding practices (aka architectural thoughts) which a very few tutors do!!

amazing work ， love it

very good explanation, amazingly written code. Awesome video.

Great video. I really like your style of explaining.

hi sir. i have a project which is to find the min value of the given fx (fx should be single-variable and can be whatever the user wants) by using genetic algorithm. for example the user enters f(x)=x^2-1 as an input and program should give us the min value for this function by using GA algorithm. i don know how to implement it . do you know where i can find such program or how can i implement it?

thank you! Appreciated

Great video! This is really useful :)

Very useful python code of genetic algorithm. Many thanks Kie!

subscribed :) keep on making more videos on genetic algorithms.

Great video! Thanks!

thank you for doing type annotations in python! That's the best kind of python!

Awesome video!!

More genetic algorithms video and preferably without type hinting as it is very very hard to follow for beginners in python. Very well explained. Subscribed and waiting for new content :)

Thanks. Very good.

5:50 definitely i will suscribe! btw i m newby at python, your code seems a radically different language (c++ likely as far as i know) XD

I would suggest to do longer videos and explain also the functions you're importing and using because most of the times we stop the video to fix function implementations. Good job though keep it up!!

Hey again, sorry if my question sounds dumb: I want to create a track for a "race" of individuals which have as their purpose to get to the end of the track. Is sensory implementation a good idea (if the next random move is hitting the wall, we should randomly assign the next move again till it no longer hits the wall and moves in an available direction, till the end of the track, meaning it is being constrained and forced to follow the path by the walls). Would this algorithm still qualified as genetic? Is it up to me how I define an individual or does it have to be as primitive as possible?

Perfect Video Man!!!

Really useful vid! I'm just getting into genetic algo. Can you make tutorial vids on PyGAD, python library for genetic algo.?

Awesome video! Also: As a rustacean I really apprechiate the type hinting ;-)

The affiliate link for kit isn't working for me (running on Windows in Chrome)

Hello, Kie Code. I tried to follow the video and type the code in my notepad++ and run it. But there is a problem of the population in run_evolution. Please help me. This is the error: Traceback (most recent call last): File "test.py", line 145, in generation_limit=100 File "test.py", line 112, in run_evolution population = populate_func() TypeError: generate_population() got an unexpected keyword argument 'generation_length' My code is: from random import choices, randint, randrange, random from typing import List, Optional, Callable, Tuple from collections import namedtuple import time from functools import partial Genome = List[int] Population = List[Genome] # To keep the seperation of functions, we use parameter to call functions FitnessFunc = Callable[[Genome], int] PopulateFunc = Callable[[], Population] SelectionFunc = Callable[[Population, FitnessFunc], Tuple[Genome, Genome]] CrossoverFunc = Callable[[Genome, Genome], Tuple[Genome, Genome]] MutationFunc = Callable[[Genome], Genome] Thing = namedtuple('Thing',['name', 'value', 'weight']) # tuple can not be changed. things = [ Thing('Laptop', 500, 2200), Thing('Headphones', 150, 160), Thing('CoffeeMug', 60, 350), Thing('Notepad', 40, 333), Thing('WaterBottle', 30, 192), ] more_things = [ Thing('Mints', 5, 25), Thing('Socks', 10, 38), Thing('Tissues', 15, 80), Thing('Phone', 500, 200), Thing('Baseball Cap', 100, 70), ] + things def generate_genome(length: int) -> Genome: return choices([0,1], k=length) def generate_population(size: int, genome_length: int) -> Population: return [generate_genome(genome_length) for _ in range(size)] def fitness(genome: Genome, things: [Thing], weight_limit: int) -> int: if len(genome)!=len(things): raise ValueError("genome and things must be of the same length.") weight = 0 # record the weight value = 0 # record the value # calculate the weight and value if it is selected. for i, thing in enumerate(things): if genome[i] == 1: weight += thing.weight value += thing.value if weight > weight_limit: return 0 return value # note: why not to simply call the fitness() because of software architecture and seperation concers. def selection_pair(population:Population, fitness_func:FitnessFunc) -> Population: return choices( population=population, weights=[fitness_func(genome) for genome in population], k=2 # this parameter draw states that we draw twice from our population to get a pair. ) def single_point_crossover(a:Genome, b:Genome) -> Tuple[Genome, Genome]: if len(a) != len(b): raise ValueError("Genomes a and b must be of same length.") length = len(a) if length < 2: return a, b p = randint(1, length-1) # select a random bit as flag. crossover the last bits between two of them. return a[0:p] + b[p:], b[0:p] + a[p:] def mutation(genome:Genome, num:int=1, probability:float=0.5) -> Genome: for _ in range(num): index = randrange(len(genome)) genome[index] = genome[index] if random() > probability else abs(genome[index]-1) return genome def run_evolution( populate_func: PopulateFunc, fitness_func: FitnessFunc, fitness_limit: int, selection_func: SelectionFunc = selection_pair, crossover_func: CrossoverFunc = single_point_crossover, mutation_func: MutationFunc = mutation, generation_limit: int = 100, )-> Tuple[Population, int]: population = populate_func() # This is the line with error for i in range(generation_limit): population = sorted(population, key=lambda genome: fitness_func(genome), reverse=True) if fitness_func(population[0]) >= fitness_limit: break next_generation = population[0:2] for j in range(int(len(population) / 2) - 1): parents = selection_func(population, fitness_func) offspring_a, offspring_b = crossover_func(parents[0], parents[1]) offspring_a = mutation_func(offspring_a) offspring_b = mutation_func(offspring_b) next_generation += [offspring_a, offspring_b] population = next_generation return population, i start = time.time() population, generations = run_evolution( populate_func=partial( generate_population, size=10, generation_length=len(things) ), fitness_func=partial( fitness, things=things,weight_limit=3000 ), fitness_limit=740, generation_limit=100 ) end = time.time() def genome_to_things(genome: Genmoe, things:[Thing]) -> [Thing]: result = [] for i, thing in enumerate(things): if genome[i] == 1: result += [thing.name] return result print(f"numberof generations: {generateions}") print(f"time: {end - start}s") print(f"best solution: {genome_to_things(population[0], things)}")

Hi kie codes, First of all, I really like your explanation of Genetic Algorithms. The things that you could improve however are the code sections. You could turn off autocorrection in you editor as it just clobbers the view of the code. Also, although the type hinting in your code does makes it more readable for seasoned python programmers, it only confuses beginners. Because the video is about Genetic Algorithms and not about type hinting, I think the cons outweigh the pros. Finally, I can follow the high level explanation of the algorithm (other video), but the code parts go to fast in my case. I hope this helps.

excellent!

yre theeee beeesssst, this was so helpfulllllll

thank you for the video. may I ask do you used roulette wheel selection for the selection?

Hello, Your video was great it helped me to understand GA, thank you and good job, Though I have a question, In your example, you demonstrate the possibilities of filling the bag with 3 Kilo, My question is if we have different bags with different sizes how can we fix this, I was trying to implement it to find the optimal solar panel stringing and the problem here that we could have multiple groups of panels with different power output need to be connected to different inverters that accept the same power so how can we decide what is the best inverter for each group some inverter could accept a different number of groups, can you provide an example of that please

These videos are awesome

i wish you could share the exact code you ran in the video and not just the template. I dont have a lot of expereience programming and ive been stuck for two days adding comments and trying to duplicate the results in the video from the version on github. Is it still available Kie?

That might help me to do that in fortran. Thanks!

Hello @Kie codes what if you you try to evenly distribute the weights evenly between n number of containers?

Amazing video ! Also: When you explaind the abs trick can you tell me the name of the song that was playing.

Where did you buy that cap?

my solution incluides the notepad and exceeds the weight limit. I have tried to copy the code as closely as possible but don't see an error. Any ideas?

What book about genetic algorithms do you recommend?

great job. think i can use this to finally solve training ai to play tetris :)

Can you please guide me for this error, i followed the complete code but having an issue in this part (I am using google colab): def fitness(genome: Genome, things: [Thing], weight_limit: int) -> int: if len(genome) != len (things): raise ValueError("genome and things must be of the same length") weight = 0 value = 0 for i, thing in enumerate(things): if genome[i]== 1: (( weight += thing.weight value = thing.value + 1 )) In this part that i have put in bracket it gives me this error : TypeError: unsupported operand type(s) for +=: 'int' and 'str'

What name is that tool you type to code ? Juputer notebook ?

Hi, when i run the code the error population, generations = run_evolution( TypeError: cannot unpack non-iterable NoneType object, may I know what I am doing wrong here

At 7:21 from where this populate_func() come from please help

What is the name of your IDE and predictive text interpreter?

Bro can you make a video of coding part for the generation of automatic timetable

for the fitness function: why use a for loop, when you could just multiply two vectors ???

when I try to code an AI I just keep getting "UserWarning: WARN: Box bound precision lowered by casting to float32"

how can i go to the genetic algorithms in python?

thanks for your amazing tutorial and coding. can we find the code anywhere?

My bro, you are a gem. Why did you stop uploading :(?

Danke für das Video! Wirklich top Qualität was du da ablieferst! Könntest du mir bitte noch verraten wo man deinen nicen Programmierhelm kaufen kann? Kannst gerne auch einen affiliate link posten falls möglich. Hab das Gefühl dann schwirren mir plötzlich die Code-Lösungen unters Dach.

I just have a question, how could I develop a "generic" genetic algorithm? like, can I use this for implementation what purposes? thanks in advance!

in your github repository i think theres a missing file. the file to define partial. because everything else is there but that one defining file.

Yo have made a nice video. But i hope u dont mind if I give a slight improvement suggestion. Your coding is really cumbersome to implement, plz make it simpler in the videos. In the source code you can implement the concepts and other best practices. For Ex: ' choices() got an unexpected keyword argument 'weights' ' - These kind of multiple errors were found when I tried to implement it in jupyter.

may i know what ide is this

Genome = List[int] # that's already something new :))))

would someone be so kind as to explain the last part where we use "partial"?

Hi, It's the perfect video. Can you please make for job shop scheduling problem?

How do i know what fitness limit should i set?

Great explanation, but your code is not easy to follow because of inability to see it when it reaches right limit or your autofinish covers things. Also my main complaint is that your code here doesn't match the code in your repository so connections can be difficult to draw.

Hi, Kie codes, I like your high-quality video. Since this is my first time learning Genetic Algorithms, I feel a little hard to follow your ideas. Nevertheless, I think it is a fantastic tutorial on this topic, and thank you for posting this video.

this was my code when I made mine import random, time class Chromosome: fitness = None genomes = [] mutation_rate = None def __init__(self, length,mut_rate): self.genomes = [random.randint(0,1) for x in range(length)] self.mutation_rate = mut_rate def calculate_fitness(self,expected_weight, object_weights): total_weight = sum([object_weights[x][1] if self.genomes[x] == 1 else 0 for x in range(len(self.genomes))]) if total_weight > expected_weight: total_weight = 0 self.fitness = total_weight/expected_weight def cross_over(self,parents): split = random.randint(0,len(parents[0].genomes)-1) self.genomes = [parents[0].genomes[x] if x

😊

What I found more interesting was your Python architecture and design. That is stuff you can't learn from google. You need to learn it from a professor. I wrote all my populate, selection, fitness, mutation functions as standalone functions, but you're saying that it's better to abstract-out the functions. Thus, if you wanted to use a different "thing" you would just change the "thing" class rather than your algorithm.

Hey, great stuff. One thing. It's pronounced nap sack. The k is silent.

Could you please post all the code in your video? Thanks a lot!

can you pls provide the source code for this , this video was very helpful

could you also make a video for GA on MATLAB plsssss…

I was examining your code and I prove it on my IDE and I don't know why [Thing] is not recognized in the list. from line 40 to along the code

Idk why brother but I found your code pretty hard and complex :(

The code download source is not complete

hello Kie, population,generations=run_evolution( populate_func=partial( generate_population,size=10,genome_length=len(things) ), fitness_func=partial( fitness,things=things,weight_limit=3000 ), fitness_limit=740, generation_limit=100 ) im facing TypeError: '

i need this code

Suggestions: don't add annoying music in the background, it disturbs the focus

Are you Polish/Slavic? Lot’s of great mathematicians and scientists here

7:45

Is it possible after 99 generations, we did not get a correct solution?

abs(value - 1) ...VERSUS... int(not value) 🙃