See also: Foundations of Biological Evolution: More Results & More Surprises
[December 5, 2024].
The Model
Why does biological evolution work? And, for that matter, why does machine learning work? Both are examples of adaptive processes that surprise us with what they manage to achieve. So what’s the essence of what’s going on? I’m going to concentrate here on biological evolution, though much of what I’ll discuss is also relevant to machine learning—but I’ll plan to explore that in more detail elsewhere.
OK, so what is an appropriate minimal model for biology? My core idea here is to think of biological organisms as computational systems that develop by following simple underlying rules. These underlying rules in effect correspond to the genotype of the organism; the result of running them is in effect its phenotype. Cellular automata provide a convenient example of this kind of setup. Here’s an example involving cells with 3 possible colors; the rules are shown on the left, and the behavior they generate is shown on the right:
Note: Click any diagram to get Wolfram Language code to reproduce it.
We’re starting from a single () cell, and we see that from this “seed” a structure is grown—which in this case dies out after 51 steps. And in a sense it’s already remarkable that we can generate a structure that neither goes on forever nor dies out quickly—but instead manages to live (in this case) for exactly 51 steps. Continue reading
