Neural Darwinism made simple

Please do find a place for DiEb's comment! ellazimm

Hey Nak, I only wish I had thought of that myself. Thanks for clearing that up! :-) tgpeeler

--Clive Hayden yes, it would be preferable to have a thread "upcoming papers on Intelligent Design". Thanks for your patience! DiEb

Mr tgpeeler, But where did the beer come from in the first place?? Don't tell me you've never heard of abeerogenesis? Nakashima

Woody says: "I meant to ask why is it that dying brain cells never kill your beliefs or keep you from rationally reasoning and scientifically theorizing about women and beer?" absolutist

Well I finally understand how Darwinism could work. heh heh. But where did the beer come from in the first place?? :-) tgpeeler

"Thanks Cliff!" says Woody. "You know Mr Peterson, it's a good thing these dying weaker brain cells don't hold any important adaptive information such as your social security number. And why is it that dying brain cells never kill your beliefs that keep you from rationally reasoning and scientifically theorizing about women and beer? Can I pour you a beer Mr. Peterson?" "A little early isn't it, Woody?" "For a beer?" "No, for stupid questions." absolutist

DieB, Keep the comments germane to the topic of the post or I will delete them. Clive Hayden

Bill, you've outdone yourself. Nice! Upright BiPed

-- Dr. Dembski, you are not easily reached :-) As I stated before, I've a problem with your concept of search in your upcoming paper "The Search for as Search". Allow me to elaborate: The Horizontal No free Lunch Theorem doesn't work for a searches of length m > 1 for a target T ⊆ Ω: Let Ω be a finite search-space, T ⊆ Ω the target - a non-empty subset of Ω. A search is a (finite) sequence of Ω-valued random variables (φ1, φ2, ..., , φm). A search is successful, if φn ∈ T for one n, 1 ≤ n ≤ m. I suppose we do agree here. Now, we look at a search Φ as a Ωm-valued random variable, i.e., Φ := (φ1, φ2, ..., , φm). When is it successful? If we are still looking for a T ⊆ Ω we can say that we found T during our search if Φ ∈ Ωm / (Ω / T)m Let's define Θ as the subspace of Ωm which exists from the representations of targets in Ω, i.e., Θ := {Ωm / (Ω / T)m|T non-empty subset Ω} Obviously, Θ is much smaller than Ωm. But this Θ is the space of feasible targets. And if you take an exhaustive partition of Θ instead of Ω in Theorem III.1 Horizontal No Free Lunch, you'll find that you can indeed have positive values for the active entropy as defined in the same theorem. But that's not much of a surprise, as random sampling without repetition works better than random sampling with repetition. But if you allow T to be any subset of Ωm, your resutls get somewhat trivial, as you are now looking at m independent searches of length 1 for different targets. The searches which you state as examples in this paper and the previous one all work with a fixed target, i.e., elements of Θ. You never mention the possibility that the target changes between the steps of the search (one possible interpretation of taking arbitrary sets of Ωm> into account). So, I'm faced with to possibilities:You didn't realize the switch from stationary targets to moving ones when you introduced searching for an arbitary subset of ΩmYou realized this switch to a very different concept, but chose not to stress the point. DiEb

Funny post. Frost122585

The National Institute of Health summarizes the numerous affects of alcohol on the brain. These are likely to substantially increase the rate of evolution! DLH

lol! Very apt analogy. tragic mishap