(this also servers as a partial response to a formal request for a response fielded by the UDer’s mortal enemies, the Pandas, specifically Dave Thomas in Take the Design Challenge!)
This is part 2 a of discussion of evolutionary algorithms. In (part 1): adventures in Avida, I exposed the fallacious, misleading, and over-inflated claims of a Darwinist research program called Avida. Avida promoters claim they refuted Behe’s notion of irreducible complexity (IC) with their Avida computer simulation. I discussed why that wasn’t the case. In addition, I pointed out Avida had some quirks that allowed high doses of radiation to spontaneously generate and resurrect life. Avida promoters like Lenski, Pennock, Adami were too modest to report these fabulous qualities [note: sarcasm] about their make-believe Avidian creatures in the make-believe world of Avida. One could suppose they refrained from reporting these embarrassing facts about their work because it would have drawn the ridicule the project duly deserves from the scientific community.
In contrast to the spectacular computation theatrics of Avida, Dave Thomas of Pandas Thumb put together a far less entertaining but no less disingenuous “proof” of the effectiveness of Darwinian evolution. Every now and then, the Panda faithful need some food to help them sustain their delusions about naturalistic evolution. This food I call Panda food, and chef Dave Thomas cooked up a pretty nice recipe to feed delusions to the faithful Pandas at our rival weblog. Perhaps if Dave Thomas refines his Panda food recipes, he should consider opening a restaurant chain, and maybe he should call it Panda’s.
To introduce what is at stake, I first introduce the idea of known explicit targets and unknown but desired targets. An explicit known target is a target which we clearly can see, describe, and precisely locate. Examples of such a target would be the bulls-eye an archer aims for.
We can build machines to help us hit such explicit targets. A good example of such an intelligently designed explicit-target hunter is the Infra-red Maverick Missile.
When on a mission to destroy something like a tank, the aircrew tasked to fly the mission, locates the explicit target (i.e. a tank), and then describes the target to the missile through the process of designation (the designation process is analogous to a point-an-click on the aircrews video screen). Upon launch, the missile employs a feedback and control strategy very akin to classical control theory to home its way to the target.
But those are examples hitting explicit targets. What about unknown, but desired targets? Let me call such targets “targets of opportunity”. A target of opportunity would be the kind of target we know inexplicitly, but still seek after. A good example of such a target of opportunity would be deer in forest during a deer hunting season. Hunters have a general strategy for tracking and hunting the deer, but they don’t know in advance what their target will be (be it Bambi or Bambi’s mother, for example). We don’t know what kind of game we may or may not bag, just that we have a general idea of what we’re striving after.
Does the military have human/machine systems with “target of opportunity” capability? Ahem. Even if I did know of such things, I’d have to deny the existence of such missiles like SLAM-ER Target-of-Opportunity Missile.
In the field of engineering and human endeavors, many of the solutions can be thought of like the process of hunting down targets of opportunity. Sometimes we are confronted with a problem, we have strategy we know in advance will yield a solution even before we explicitly know what the solution is.
A VERY simple case in point. Take the integers from 1 to 1000. The following question is posed to us, “what is the sum of these integers, 1 to 1000?” Do we have to know in advance what the answer is? Maybe, maybe not. I’ll cheat and give you the answer. It’s 500,500.
The important point is, that even if you did not know the answer (the target of opportunity) in advance, you have well-proven strategies to find and hit the target. One such strategy would be to sit down with a calculator or spread sheet and add the numbers form 1 to 1000. Another would be to write a computer program which added them together. Yet another would be to write a genetic algorithm to find the answer. I’ll provide several such examples a the end of this essay for you computer geeks out there! But the most important thing in hitting such a target of opportunity is that by intelligently designing the right strategy, one can hit a target of opportunity without the target being explicitly described. Get the picture?
Adding of numbers is a very primitive example of hunting down a target of opportunity. A far more sophisticated example, is finding the optimal design of a computer chip given certain constraints. The space of possibilities is extremely large, but engineers can program genetic algorithms (much like they build sophisticated calculators) to hunt down solutions on their behalf.
Back to the Pandas challenge of me. To build their case, anti-IDers will often need to equivocate and obfuscate the issues. Clarity is their enemy, confusion is their friend. Such was the recent offering by Dave Thomas of Pandas in a long, tedious essay, Target? TARGET? We don’t need no stinkin’ Target!.
He shows how a genetic algorithm can hunt down a target of opportunity. But as I hope I’ve shown, such a thing is unremarkable! However, he hints his program demonstrates mindless forces can find such targets without intelligent design.
Dave employs equivocation and Orwellian Double Speak to argue his case. He takes a designed selection strategy and tries to pass it off as an example of mindless undesigned forces which can magically converge on a target of opportunity. How does he promote his theatrical gimmick? Read what he says, and then read the challenge he poses to IDers:
Genetic Algorithms are simplified simulations of evolution that often produce surprising and useful answers in their own right. Creationists and Intelligent Design proponents often criticize such algorithms for not generating true novelty, and claim that these mathematical recipes always sneak the “answer†into the program via the algorithm’s fitness testing functions.
There’s a little problem with this claim, however. While some Genetic Algorithms, such as Richard Dawkin’s “Weasel†simulation, or the “Hello World†genetic algorithm discussed a few days ago on the Thumb, indeed include a precise description of the intended “Target†during “fitness testing†on of the numerical organisms being bred by the programmer, such precise specifications are normally only used for tutorial demonstrations rather than generation of true novelty
I have placed the complete listing of the Genetic Algorithm that generated the numerous MacGyvers and the Steiner solution, at the NMSR site.
If you contend that this algorithm works only by sneaking in the answer (the Steiner shape) into the fitness test, please identify the precise code snippet where this frontloading is being performed.
Thomas sneaks the answer in by intelligently designing a strategy which will find the target of opportunity. This sort of gimmickry is not much beyond the following illustration:
One kid goes up to another with a paint ball gun and shoots him, and says,
“Don’t get mad, I wasn’t aiming at you, I was aiming at the shirt you were wearing.â€Â
By giving the computer the correct strategy (like a method of adding numbers) one guarantees the answer (or target) will be hit, or at least a near miss. There are numerous strategies which will succeed, but they still must be intelligently designed. For the less technically minded readers, I hope what I’ve written so far gives a narrative explanation of what’s really going on.
To get an idea of how easy it would be to give the wrong search strategy, consider a long sequence of driving directions. If even one occurence of the word “left” is substitutted for “right” or vice versa, the directions will fail. Without intelligence programming the selection strategy, the target would have missed in Dave’s program. However, Dave Thomas used intelligence to ensure a miss wouldn’t happen, or at least, less likely. He thus snuck the answer in after all, contrary to his denials.
In the post script, for the benefit of the technically minded readers, I’ll address the more technical details to help put all of Dave’s nonsense to rest.
Salvador Cordova
PS
TECHNICAL DETAILS
Dave’s Challenge:
If you contend that this algorithm works only by sneaking in the answer (the Steiner shape) into the fitness test, please identify the precise code snippet where this frontloading is being performed.
I’ll identify it plain and simple, and call his bluff. The major front loading is in how selection is made. With the wrong selection description, the wrong target of opportunity, if any, will be hit. Simple!
Dave counts on a bit of obfuscation to make his work unreadable. He chooses an antiquated computer language known as FORTRAN to make his demands. “Lets invite UD software engineers to read my hieroglyphics and invite them to show where I sneaked the answer in!” Sheesh.
That said, I will identify an important part of his barely readable code, which, if removed will cause the genetic algorithm to miss the target. The fact that this section is essentially irreducibly complex is testament that intelligent design was needed to enable the genetic algorithm to do its thing.
If any section is even slightly re-written in a mindless way, the program likely misses the target at best and fails to even functionally compile at worst. I’m sorry the following link will look like hieroglyphics to some, but of necessity, I need to show it to call Dave’s bluff with it. Here is one of the many places where Dave sneaks the answer in:
Does Dave Thomas doubt me that I’ve identified where he snuck the answer in? How about we allow 5 random changes to the code segment I pointed to? Does he think such mindless modification can be introduced and the algorithm will still function? Do we think the GA will successfully hit the target (assuming the GA can even run) in the midst of 5 measly random changes? Will Dave run away from the fact that the above selection strategy needs intelligent design? Or will he represent that the above code segment came to be of its own accord, and that the selection strategy described by the above code is the product of blind mindless processes?? Will he continue to insist what he did is not sneaking the answer in?
The selection strategy in his program is anything but natural. Just because the terms Darwinian and selection are used in the argument does not mean intelligent agency is not permeating the entire project. Such labelings are doublespeak. If I went through and re-labeled everything intelligently designed selection vs. natural selection, you’d get the real gist of what’s happening!.
All right, as I promised, I’ll now present several ways to add the numbers 1 to 1000 and get the answer 500500. With the exception of the first program, in each case the target answer will not be an explicitly stated target, but rather a target of opportunity which is hit via an intelligently designed hunting strategy.
The sample programs are written in the C language.
This program will give the explicit answer to question, “What is the sum of the numbers from 1 to 1000?” :
This program will give the answer to question, “What is the sum of the numbers from 1 to 1000?” through a brute force computation which involved adding all the numbers from 1 to 1000:
This program will give the answer to question, “What is the sum of the numbers from 1 to 1000?” through Gauss’s method of mathematical induction:
This program will give the answer to question, “What is the sum of the numbers from 1 to 1000?” through recursive addition of all the numbers from 1 to 1000 :
This program will give the answer to question, “What is the sum of the numbers from 1 to 1000?”
through a genetic algorithm. The algorithm pairs up numbers form 1 to 1000. Rather than compute the midpoint via a simple calculation it takes a random number as a starting point and then mutates the random number and uses a fitness function to select between the mutant and the original number to give the current best midpoint estimate. The process is repeated with increasing refinement. 2 times the sum of the midpoints then becomes the sum we are seeking. Snapshots of the algorithm’s progress are given along the way. The following computational theatrics are akin to what Dave Thomas performed:
PPS
I and my co-workers (while I was in school in the 90’s) worked on target recognition systems and simulations of missile guidance systems. Dave can feed the biologists at Pandas Thumb his Panda food, but half the UDers here have relevant engineering backgrounds to see through the charade. He could not have picked a worse thing to do than challenge the UDers to disprove the flimsy claims of his intelligently designed program.