tldr; chunkwm has been completely rewritten and is now yabai

From the chunkwm site:

chunkwm is no longer in development because of a C99 re-write, yabai.

yabai was originally supposed to be the first RC version of chunkwm. However due to major architectural changes, supported systems, and changes to functionality, it is being released separately. There are multiple reasons behind these changes, based on the experience I've gained through experimenting with, designing, and using both kwm and chunkwm. Some of these changes are performance related while other changes have been made to keep the user experience simple and more complete, attempts to achieve a seamless integration with the operating system (when possible), proper error reporting, and yet still keep the property of being customizable.

For those who don't know, chunkwm was / is a tiling windows manager that is sort of like bspwm / i3 etc. I've been using chunkwm for a few months now and love it. If you're also an i3 user, the lack of a proper super key does make your key combos different, but overall its an excellent window manager. Both chunkwm and yabai use koekeishiya's Simple Hotkey Daemon (skhd).

Anyway, I gave the new version the day and its pretty good, but still has some quirks. It seems like development is moving along quickly, so keep an eye on it.

Introduction to Genetic Algorithms

Genetic algorithms can be used to solve problems that are difficult, or impossible to solve with traditional algorithms. Much like neural networks, they provide good-enough solution in short amount of time, but rarely find the best one. While they're not as popular as neural networks nor as widely used, they still have their place, as we can use them to solve complicated problems very fast, without expensive training rigs and with no knowledge of math.

Genetic algorithms can be used for variety of tasks, for example for determining the best radio antenna shape, aerodynamic shapes of cars and planes, wind mill shapes, or various queing problems. We'll use it to print "Hello, World!".

How does it work?

Genetic algorithm works in three steps.

1. Generate random solutions
2. Test how good they are
3. Pick the best ones, breed and mutate them, go to step 2

It works just like evolution in nature. First, we generate randomised solutions to our problem (in this case: random strings of letters).

Then, we test each solution and give it points, where better solutions gain more points. In our problem, we would give one point for each correct letter in the string.

Afterwards, we pick the best solutions and breed it together (just combine the strings). It's not bad idea to mutate (or randomize) the string a bit.

We collect the offsprings, and repeat the process until we find good enough solution.

Generate random solutions

First of all, we need to decide in which form we will encode our solutions. In this case, it will be simply string. If we wanted to build race cars, we would encode each solution (each car) as array of numbers, where first number would be size of the first wheel, the second number would be size of the second wheel, etc. If we wanted to build animals that try to find food, fight and survive, we would choose a decision tree (something like this).

So let's start and make few solutions, or entities. One hundred should be enough.

from random import randint

goal = "Hello, World!"
allowed_characters = list("qwertyuiopasdfghjklzxcvbnmQWERTYUIOPASDFGHJKLZXCVBNM ,!")

def get_random_entity(n, string_length):
    entities = []
    for _ in range(0, n):
        entity = ""
        for _ in range(0, string_length):
            entity += allowed_characters[randint(0, len(allowed_characters)-1)]
        entities.append(entity)
    return entities

print(get_random_entity(100, 13))

Test how good they are

This is called a "fitness function". Fitness function determines how good a solution is, be it a car (travel distance), animal (food gathered), or a string (number of correct letters).

The most simple function we can use right now will simply count correct letters. If we wanted, we could make something like Levenshtein distance instead.

def get_fitness(entity):
    points = 0
    for i in range(0, len(entity)):
        if goal[i] == entity[i]:
            points += 1
    return points

Crossover and mutation

Now it's time to select the best ones and throw away the less fortunate entities. Let's order entities by their fitness.

Crossover is a process, when we take two entities (strings) and breed them to create new one. For example, we could just give the offspring one part from one parent and another part from second parent.

There are many ways how to do this, and I encourage you to try multiple approaches when you will be doing something like this.

P:  AAAABBB|BCCCC
P:  DDDDEEE|FGGGG

F1: AAAABBB|FGGGG

Or we can just choose at random which letter will go from which parent, which works the best here. After we have the offsprint (F1), we should mutate it. What if we were unfortunate, and H (which we need for our Hello, World!) was not in any of the 100 entities? So we take the string and for each character of the string, there is a small chance to mutate it - change it at random.

F1:  ADDDEBEFGCGG
F1`: ADHDEBEFGCGG

And it's done. Now kill certain part of old population. I don't know which percentage is best, but I usually kill about 90% of old population. The 90% that we killed will be replaced by new offsprings.

There is just one more thing: which entities do we select for crossover? It isn't bad idea - and it generally works just fine - to just give better entities higher chance to breed.

def get_offspring(first_parent, second_parent, mutation_chance):
    new_entity = ""
    for i in range(0, len(first_parent)):
        if randint(0, 100) < mutation_chance:
            new_entity += allowed_characters[randint(0, len(allowed_characters)-1)]
        else:
            if randint(0, 1) == 0:
                new_entity += first_parent[i]
            else:
                new_entity += second_parent[i]
    return new_entity

When we add everything together, we get this output:

Generation 1, best score: 2 ::: QxZPjoptHfNgX
Generation 2, best score: 3 ::: XeNlTOQuAZjuZ
Generation 3, best score: 4 ::: weolTSQuoZjuK
Generation 4, best score: 5 ::: weTgnC uobNdJ
Generation 5, best score: 6 ::: weTvny uobldb
Generation 6, best score: 6 ::: HellSy mYbZdC
Generation 7, best score: 7 ::: selOoXBWoAKn!
Generation 8, best score: 8 ::: HeTloSoWYZlh!
Generation 9, best score: 8 ::: sellpX WobKd!
Generation 10, best score: 9 ::: welloq WobSdb
Generation 11, best score: 9 ::: selloc WoZjd!
Generation 12, best score: 10 ::: wellxX WoVld!
Generation 13, best score: 10 ::: welltX World!
Generation 14, best score: 10 ::: welltX World!
Generation 15, best score: 10 ::: welltX World!
Generation 16, best score: 11 ::: zellov Wobld!
Generation 17, best score: 11 ::: Hellty World!
Generation 18, best score: 11 ::: welloX World!
Generation 19, best score: 11 ::: welloX World!
Generation 20, best score: 11 ::: welloX World!
Generation 21, best score: 12 ::: welloX World!
Generation 22, best score: 12 ::: Helloy World!
Generation 23, best score: 12 ::: Helloy World!
Generation 24, best score: 12 ::: Helloy World!
Generation 25, best score: 12 ::: Helloy World!
Generation 26, best score: 12 ::: Helloy World!
Generation 27, best score: 12 ::: Helloy World!
Generation 28, best score: 12 ::: Helloy World!
Generation 29, best score: 12 ::: Helloy World!
Generation 30, best score: 12 ::: Helloy World!
Generation 31, best score: 12 ::: Helloy World!
Generation 32, best score: 12 ::: Helloy World!
Generation 33, best score: 12 ::: Helloy World!
Generation 34, best score: 13 ::: Helloy World!
Generation 35, best score: 13 ::: Hello, World!

As we can see, we find pretty good solution very fast, but it takes very long to find perfect solution. The complete code is here.

Maintaining diversity

When we solve difficult problems, it starts to be increasingly important to maintain diversity. When all your entities are basically the same (which happened in this example), it's difficult to find other solutions than those that are almost the same as the currently best one. There might be a much better solution, but we didn't find it, because all solutions that are different to currently best one are discarded. Solving this is the real challenge of genetic algorithms. One of the ideas is to boost diverse solutions in fitness function. So for every solution, we compute distance to the current best solutions and add bonus points for distance from it.

Has anybody had much experience playing DnD or other tabletops with children? I've been toying with the idea of making a fairly straightforward and simplified RPG using Story Cubes and GURPS that kids can get involved with easily and have fun playing. I'm specifically aiming to play with my daughter (8) and my niece (5) on a big family holiday in August, though I see no real reason that this couldn't work with adults as well.
Essentially, the conceit would go along the lines of each player rolling a limited number of story dice to help with character creation and such. I'd ask the players a few simple questions about their powers (for example, are you more of a wizard or more of a warrior?) to get some basic stats stats together (STR, DEX, INT, CON), and then use story dice myself to quickly improvise a short one-shot session.

Does anyone have experience playing with kids, and if so - any pointers? Am I being too ambitious about children's ability to imagine stuff in this way? If so, are there any good systems out there that are good for young people to pick up and get stuck into roleplaying with?