Welcome to Quantum Magazine's podcast.

Each episode we bring you stories about developments in science and mathematics.

I'm Susan Vallett.

Creationists insist that evolution had to assemble upward of 300 amino acids in the right order to create just one medium-sized human protein.

That's more than 20 to the 300th power of possibilities to sift through.

It's improbable that evolution could have stumbled onto the correct combination through

random mutations within even billions of years, they say.

The fatal flaw in their argument is that evolution didn't just test sequences randomly.

The process of natural selection winnowed the field more systematically.

Plus, it seems likely that nature somehow also found other shortcuts.

Computer scientists face similar challenges with problems that involve finding optimal solutions

among astronomically huge sets of possibilities.

Some are looking to biology for inspiration, even as biologists still try to figure out exactly how life does it, too.

Genetic algorithms are optimization methods that have been popular for decades.

They use the principles of natural selection to engineer new designs for things like robots,

drugs, and transportation systems.

These algorithms also train neural networks and encrypt and decrypt data.

The technique starts by treating random solutions to a problem as organisms.

Here's computer scientist Hector Zenil of the Karolinska Institute

in Sweden.

Obviously these organisms are oversimplified versions of real organisms.

But at the end, they are not completely strange to them because we know all life on earth,

...