You are viewing revision 0.0.18, last edited by pedrochaves

An optimization process is any kind of process that systematically comes up with solutions that are better than the solution used before. More technically, this kind of process is one that performs searches in a large search space, hitting small, low probability targets. When this process is gradually guided by some agent into some specific state, through searching specific targets, we can say it prefers that state.

The best way to exemplify an optimization process is through a simple example: Eliezer Yudkowsky suggests natural selection is such a process. Through an implicit preference – better replicators – natural selection searches all the genetic landscape space and hit small targets: efficient mutations.

Consider the human being. We are a rather unlikely object to have come about by chance, and so of course, it didn't. Natural selection, over millions of years, built up the infrastructure needed to build a full functioning body, and it ended up creating it, because people are rather efficient replicators. Or consider the famous chessplaying computer, Deep Blue. Outside of the narrow domain of selecting moves for chess games, it can't do anything impressive: but as a chessplayer, it was massively more effective than virtually all humans. Humans or evolution are more domain-general optimization processes than Deep Blue, but that doesn't mean they're more effective at chess specifically. (Although note in what contexts this optimization process abstraction is useful and where it fails to be useful: it's not obvious what it would mean for "evolution" to play chess, and yet it is useful to talk about the optimization power of natural selection, or of Deep Blue.)...

(Read More)