And now, we can get a number of the efficiency for

each and we can take the best half of them, these two for example.

And then, we can combine their parameters into a new design.

So this is called crossover and

it's inspired again by genetics in which chromosomes actually meet together.

And they crossover at random points and they exchange material.

So now, our designs are going to be paired up, the best designs, and

they're going to crossover.

So we'll take half of each design with a random crossover point,

and we'll generate a new baby design.

[LAUGH] And that's going to fill out the generation and

fill out the population for the next round of testing or the next generation.

So what this procedure does, essentially, is in a rough fitness landscape.

Combining the two designs takes jumps across the fitness landscape and

parameter space.

So we don't have to search through every possible set of parameters,

we're making jumps.

And what that means is that we can jump over a local optima, valleys, if you will,

so that we can be higher on the hill at the end and find the global optimum.

So pretty cool [LAUGH].