Viewing a single comment thread. View all comments

MrRumfoord t1_ivcew10 wrote

Reply to comment by guyonahorse in Insects get stuck in a spider's web, why doesn't a spider get stuck in its own web? by Koning_Health

I'd like to see the paper airplane after 4 billion years of this process.

67

0hip t1_ivcp0zy wrote

Just look at birds. Or bats. Or insects. They are the result of this process

98

ACTM t1_ivcqc6f wrote

How many folds for a robin then?

32

0hip t1_ivcqgtm wrote

With 4 billion years why would you still use paper?

27

Dumfing t1_ivdnc8l wrote

Because evolution struggles to make large leaps with long term changes in mind (why are we still carbon based since the dawn of time?)

3

Apokolypze t1_ive1z3m wrote

I always thought that was just nature doing it's thing cuz carbon based life forms recycle pretty good.

1

jollyspiffing t1_ivim8r4 wrote

Instructions unclear. How do I unfold a pigeon?

1

0hip t1_ivj1xeq wrote

Start with the wings are there’s a secret button the government dosent want you to know about. It will unfold itself from there

1

CodingLazily t1_ivcphzs wrote

I don't know about paper airplanes, but here's an antenna that is the result of a supercomputer running simulations with an evolutionary algorithm.

https://en.wikipedia.org/wiki/Evolved_antenna

​

Evolutionary algorithms are basically the same as evolution, but to summarize evolutionary algorithms for those who aren't CS majors: First, you generate a random population of candidates. You test each candidate for fitness to see which ones best meet the criteria and survive the generation (in this case by simulating the antenna.) You take the top few candidates and populate a new generation with a few thousand descendants that randomly mix a little of each parent and a small amount of pure random mutation. After hundreds or thousands of generations, you display the most highly evolved candidate.

25

Brainsonastick t1_ivcs3cn wrote

You can simulate it! There’s a class of algorithms called genetic algorithms. Basically, you write a code to convert from a string of characters to an object whose fitness you can test. In this case, a string of positions at which to fold the paper.

Then, you generate a bunch at random, test their fitness, and have the best ones interbreed. That means combining their strings (in a meaningful way) and adding random mutations.

That’s your new generation. Keep doing this over and over again and you’ll find the fitness of your planes, on average, increases each generation. After enough generations, you’ll get some pretty decent planes (assuming your code is good). Some might even be recognizable but a lot will super weird and surprising that they work at all.

There’s a simple video of a genetic algorithm evolving simple cars. It generates one capable of completing the track in only 37 generations. That sounds like a long time but on an evolutionary scale, it’s nothing. On a computer simulation time scale, it’s nothing.

And here is a more complex car evolution video that goes into some easily accessible explanation on how it works

8

popbottle159 t1_ivcp4ed wrote

Look up the side view of a stealth bomber and a diving falcon. One is human calculations and wind tunnels. The other is pure nature streamlining the best way for high speed flight.

3

Nyatwit t1_ivdu5oa wrote

Aka dragonflies.. They are not exactly on the top of the evolutionary chart but some of the most efficient fliers. The can fly forwards, backwards and hover.

1