: Engineering mathematics. Engineering analysis. (TA347
: Evolutionary computation. Information technology–Mathematics.1
Yes, Tom English was right to warn us not to buy the book until the authors establish that their mathematical analysis of search applies to models of evolution.
But some of us have bought (or borrowed) the book nevertheless. As Denyse O’Leary said: It is surprisingly easy to read. I suppose she is right, as long as you do not try to follow their conclusions, but accept it as Gospel truth.
In the thread Who thinks Introduction to Evolutionary Informatics should be on your summer reading list? at Uncommon Descent, there is a list of endorsements – and I have to wonder if everyone who endorsed the book actually read it. “Rigorous and humorous”? Really?
Dembski, Marks, and Ewert will never explain how their work applies to models of evolution. But why not create at list of things which are problematic (or at least strange) with the book itself? Here is a start (partly copied from UD):
I’ll follow Patrick’s lead and offer a few comments on another paper from the Evolutionary Informatics Lab. The paper analyzes Tom Schneider’s ev program, and while there are several problems with the analysis, I’ll focus on the first two sentences of the conclusions:
The success of ev is largely due to active information introduced by the Hamming oracle and from the perceptron structure. It is not due to the evolutionary algorithm used to perform the search.
To explain the authors’ terminology, active information is defined quantitatively as a measure of relative search performance — to say that something provides N bits of active information is to say that it increases the probability of success by a factor of 2N. The Hamming oracle is a function that reports the Hamming distance between the its input and a fixed target. The perceptron structure is another function whose details aren’t important to this post. Figure 1 shows how these three components are connected in an iterative feedback loop.