There’s a useful metaphor for this process, from a computing technique mathematicians sometimes use to find approximate solutions to numeric problems called “simulated annealing”. Consider a graph with high points (called “maxima”) and low points (called “minima”) like this one:
Sometimes you know the equation, and can just solve it. But, at other times, the situation is like having a black box with some dials to twiddle, and a single output (which you want to be as big as possible). One way to search for the dial setting that produce the biggest output would be to set the dials all to zero then start systematically searching through all the possible settings, but that might take years. If the graph is simple, you can usually find the answer much faster by noting how large the output is for ten different random settings, then concentrating your search near the random setting that had the largest output and making some smaller random changes, narrowing down on the best of those, and then making some last very small changes to fine-tune your solution. This process is known as “simulated annealing” and the amount of random noise you use to vary the solution at each stage is known as the ‘temperature’. You start off at a high ‘temperature’, making big random jumps, then slowly cool things down, making smaller and smaller changes:
If you lower the ‘temperature’ too fast, you can get stuck at a local maxima. To make the shift to a different maxima (perhaps a higher one), you’d have to increase the ‘temperature’ again.
Dawkins goes into details in his book “The Greatest Show on Earth” about how DNA isn’t a blueprint—rather it is a series of instruction on how to do 3D origami. And the earlier an instruction is in the sequence, it harder it is to vary yet still come up with a functional end shape. (This is why there are local maxima that evolution finds it difficult to vary away from to perhaps better solutions that a designer could have found—such as not routing a nerve in a Giraffe’s neck down via the heart before returning half way back up it again.)
There’s a useful metaphor for this process, from a computing technique mathematicians sometimes use to find approximate solutions to numeric problems called “simulated annealing”. Consider a graph with high points (called “maxima”) and low points (called “minima”) like this one:
IMAGE
Sometimes you know the equation, and can just solve it. But, at other times, the situation is like having a black box with some dials to twiddle, and a single output (which you want to be as big as possible). One way to search for the dial setting that produce the biggest output would be to set the dials all to zero then start systematically searching through all the possible settings, but that might take years. If the graph is simple, you can usually find the answer much faster by noting how large the output is for ten different random settings, then concentrating your search near the random setting that had the largest output and making some smaller random changes, narrowing down on the best of those, and then making some last very small changes to fine-tune your solution. This process is known as “simulated annealing” and the amount of random noise you use to vary the solution at each stage is known as the ‘temperature’. You start off at a high ‘temperature’, making big random jumps, then slowly cool things down, making smaller and smaller changes:
IMAGE
If you lower the ‘temperature’ too fast, you can get stuck at a local maxima. To make the shift to a different maxima (perhaps a higher one), you’d have to increase the ‘temperature’ again.
Dawkins goes into details in his book “The Greatest Show on Earth” about how DNA isn’t a blueprint—rather it is a series of instruction on how to do 3D origami. And the earlier an instruction is in the sequence, it harder it is to vary yet still come up with a functional end shape. (This is why there are local maxima that evolution finds it difficult to vary away from to perhaps better solutions that a designer could have found—such as not routing a nerve in a Giraffe’s neck down via the heart before returning half way back up it again.)