Genetic Algorithms for Uncommonly Dense Software Engineers

Very nice! Is the Windows version of the program availaible?

I'm wondering, when is a fixed target not a fixed target? I've worked on simple GA's that are used to program FPGAs. The task was go generate a 0V signal on the input of a 1kHz square waveform and a 5V signal on the input of a 10kHz wave. A trivial task for a human to design, except we weren't using any supporting RC circuitry or clocks, only power to the FPGA being used.

Obviously, the above phenotype was trivially simple enough to just dump into the fitness function. That would appear to be a fixed target then. However, at the same time, the solution was different every time. This isn't surprising, as the GA effectively treated the FPGA as an analog device, thus producing a large collection of "solutions".

I think the above is a good example of how even supposedly fixed target GA's can produce differing results. Also, it's interesting to note that the solutions our particular GA produced resembled in no way anything that a human would have come up with. It completely ignored all common concepts of circuit design to come up with often bizarre signal pathways, with some sections detacted from the main circuit through the chip entirely and working merely by apparent electromagnetic coupling.

Anyway, enough of my babble! I really enjoyed reading this series of articles!

Andrew

Dave Thomas wrote: What is it going to be like having to go to Bill Dembksi and admit that you've learned the hard way of the true meaning of what Daniel Dennett terms Leslie Orgel's Second Law: "Evolution is smarter than you are"?

well, there is an instance where a fixed seed is (well, was) actually a good thing. late 70s/early 80s video games. timer chips were cheap (and essential to the early calculators), but true clock chips with memory were epensive back then so they couldn't rely on that to generate a seed. instead, they experimented, picked the one that seemed the "fairest", and went with it. home consoles didn't have memory clocks either (e.g., Atari 2600) so those games also hard-coded their seed.

sometimes, the seed would be re-generated using user reactions (or even their score at various times), so if the user missed shooting something, it would generate different objects to shoot at from that point on (air sea battle). however, if the user did exactly the same thing, then the game would do the same thing in response.

Its the reason you could buy a book on "How to Win at Pac Man". they analysed the patterns that repeated if the player was consistent with their actions.

some games like card game cartridges also randomized by looking at "how many milliseconds has the machine been turned on" to give more variety. a card game isn't much fun if it deals the same deck every time. ;-)

I'm wondering, when is a fixed target not a fixed target? I've worked on simple GA's that are used to program FPGAs. The task was go generate a 0V signal on the input of a 1kHz square waveform and a 5V signal on the input of a 10kHz wave. A trivial task for a human to design, except we weren't using any supporting RC circuitry or clocks, only power to the FPGA being used. Obviously, the above phenotype was trivially simple enough to just dump into the fitness function. That would appear to be a fixed target then.

I'm surprised this challenge project caused much of a stir in ID circles. I thought that most ID adherents would accept the idea of microevolution, which is what this sort of optimization problem is similar to. If so, I would expect the discourse to shift from "GAs need a fixed target to work." towards "GAs only show that function optimization is a kind of microevolution, but can't demonstrate features of the natural world such as speciation."

I don't think you can answer that objection with a discussion of niching in GAs. My understanding of niching is that it is more similar to a mixed strategy within a species in evolutionary terms.

Obviously, ALife environments such as Tierra demonstrate speciation via mutation when parasites appear, but I'm sure most IDers would not think that parasites are an uptick in complexity, and therefore can be ignored. If mutation is the only operator changing the genotype, speciation is going to be very slow, or not show the growth in complexity we see in nature, IMHO.

I don't know of any EC studies that show speciation (no interbreeding based on phenotype differences). If anyone has a reference, please share it. Algorithms that compute genotype distances and only allow close genotype matches to mate are going to be open to the argument that the algorithm has "baked in" the speciation rather than showing that speciation is an emergent phenomenon of the run.

My intuition is that a good EC speciation demonstration will require a large number of generations (like nature) and/or demes whose connectivity changes over time (like nature) and/or co-evolution (the rest of the population is part of the fitness function, like nature).

BTW, my prediction is that after "GAs can't evolve species.", the next issue will be "GAs can't evolve sex." All GAs assume sexual reproduction is available as part of the suite of genetic operators - it doesn't emerge. That's baking in the answer because "male and female He created them." If you use God's tools, of course you're going to get similar results. But can you make God's tools?

All GAs assume sexual reproduction is available as part of the suite of genetic operators - it doesn't emerge.

Actually, many GAs use asexual reproduction. This is not a problem - most of the time, sexual reproduction doesn't perform massively better. The reason for this is that, whilst sexual reproduction increases variety, it destroys perfection just as quickly.

The main reason we humans have sexual reproduction isn't because it's better for evolution - it's because it makes life harder for parasites, who have trouble specialising enough to attack one generation whilst generalising enough to attack the next. A good book for this stuff is "The Red Queen" by Matt Ridley.

4. Natural selection might be capable of being simulated on computers, and the simulations may demonstrate good capacity for solving some problems in optimization, but the optimization problems are not as complex as those in actual biology. This objection typically appears once the first three above have been disposed of. Computer simulation, once held to be either a potential indicator of merit or an actual falsifier of natural selection, is then treated as essentially irrelevant to natural selection. It is certainly true that computer simulations are less complex than biological problems, but the claim at issue is not that EC captures all the nuances of biology, but rather that EC gives a demonstration of the adaptive capabilities of natural selection as an algorithm.

All GAs assume sexual reproduction is available as part of the suite of genetic operators - it doesn't emerge. Actually, many GAs use asexual reproduction. This is not a problem - most of the time, sexual reproduction doesn't perform massively better. The reason for this is that, whilst sexual reproduction increases variety, it destroys perfection just as quickly.

The general strategy of taking up issues in biology that aren't the focus of evolutionary computation was addressed in my post here a bit earlier this month:

Very nice! Is the Windows version of the program available?

— Marek 14

What was Salvador Cordova using if not A COMPUTER??? His excuse was too pitiful to even be laughable.

Andrew,

I read an article about your FPGA experiment. Interesting stuff. I believe the article said you were using an asynchronous FPGA, and so the design was very sensitive to temperature and power supply voltage. Did you ever get a synchronous version working?

Is Sal really this dim, or is he just dishonest?

Dave Thomas --- Well done again!

You forgot one of the major criteria, the same algorithm given a different problem will still find a solution.

The example might be clearer if simplify the problem by not using long strings. I started playing with my own model when I say the contest.

Define the environment as an arbitraty rectangle (say 0,0 to 500,500)
Selectors are defined a series of points (arbitrary or random)
Define a critter as a series points connected by line segments.

Fitness:
Subtract the total length of all line segments in the critter.
Subtract the distance from the each selector to the nearest point in the critter multiplied by constant (say 100 so distance is more important than length)

Mutation:
Addition: select a random point in the critter and add a line segment to a new random point within radius x (say 10)
Reduction: select a random point and remove it from the critter. Integrity must be maintained so new line segments are created between attached points. (ie. A-B-C becomes A-C or A-B-C and B-D becomes A-C and A-D and C-D, etc).
Alteration: A given point is moved to a new random location within radius x (say 3)

Selection:
Keep the best 3rd of all variations

Given enough generations this will approximate a solution.

You forgot one of the major criteria, the same algorithm given a different problem will still find a solution.

— Alann

Andrew, I read an article about your FPGA experiment. Interesting stuff. I believe the article said you were using an asynchronous FPGA, and so the design was very sensitive to temperature and power supply voltage. Did you ever get a synchronous version working?

— bob

Andrew: thanks, I've been looking for that for a while. Had found Adrian Thompson's site, but couldn't locate that particular demonstration.

But, given a problem with no obvious answer, and one for which a Genetic Algorithm actually out-performed ID theorist Cordova, the fall-back position is "But Computers are FASTER than humans!"

Which would seem to be the point to using them.

But, given a problem with no obvious answer, and one for which a Genetic Algorithm actually out-performed ID theorist Cordova, the fall-back position is "But Computers are FASTER than humans!" Which would seem to be the point to using them.

As a professional software developer I would just like to say how astounded I am that anyone claiming to know anything about software dvelopment would use a hard coded constant in a call to srand(). This indicates a basic lack of knowledge of both C and RNG's.

Furthermore is Cordova claiming this program works? It is riddled with significant errors and most certainly will not compile.

As a professional software developer I would just like to say how astounded I am that anyone claiming to know anything about software dvelopment would use a hard coded constant in a call to srand(). This indicates a basic lack of knowledge of both C and RNG's.

Let me suggest than you replace "uncommonly" with "inordinately" so as to create a more fitting acronym.

"Uncommonly Dense" refers to "Uncommon Descent", Demski's blog where much of this insanity occurs. "Uncommon Dissent" also works, both in how science looks at ID and how the blog is administrated.

I've developed this guide to help the folks over there figure out if the Genetic Algorithms (GAs) they are working on employ a "fixed-target" approach (like Dawkins's Weasel), or if they are instead un-targeted, like most GAs used in research and industry are.

1) Check That Random Seed

2) Is the Answer Always the Same?

3) Is it just that Computers are Faster?

To put it more simply, how can one tell that a fitness function f:C->[0-1], where C is the space of all critters, is targeted or encodes the solution? My guess is that in general one cannot, but also that it doesn't matter.

I've been writing software for over 40 years, C for about 30, and I have often used a hard coded constant in a call to srand() --- when I need reproducibility (you can omit the call to srand() altogether in that case, but I prefer to make it explicit, and I still have habits from before such things were specified in the standard --- before there was a standard, in fact). Of course, it does not follow that Cordova has basic knowledge of C or RNG's.

To put it more simply, how can one tell that a fitness function f:C->[0-1], where C is the space of all critters, is targeted or encodes the solution? My guess is that in general one cannot, but also that it doesn't matter.

...claim that ALL GAs require specification of a precise "Target." [emphasis in original]

Having reflected upon this further, I don't even know what the claim means. Particularly the claim that the fitness function must specify a "Target". Does it mean that there is one and only one optimum in the fitness space?

— GuyeFaux

And nevertheless, it remains the case that no genetic algorithm or evolutionary computation has designed a complex, multipart, functionally integrated, irreducibly complex system without stacking the deck by incorporating the very solution that was supposed to be attained from scratch (Dawkins 1986 and Schneider 2000 are among the worst offenders here).

— Dembski

Nevertheless, as shown elsewhere (Meyer 1998:127-128, 2003:247-248), these [genetic algorithm] programs only succeed by the illicit expedient of providing the computer with a "target sequence" and then treating relatively greater proximity to future function (i.e., the target sequence), not actual present function, as a selection criterion.

— Meyer

Comment #110272 Posted by Marek 14 on July 6, 2006 02:27 AM A suggestion: how about running the simulation again, this time with a more complex set of points where Steiner solution is neither as symmetrical, neither actually known in advance? In that case, front-loading the solution is not just a silly idea - it's actually impossible.

...treating relatively greater proximity to future function (i.e., the target sequence), not actual present function, as a selection criterion.

30 years? Since 1976? Since 2 years before K&R was published? So when did you leave Bell?

30 years? Since 1976? Since 2 years before K&R was published?

So when did you leave Bell?

Yes, there can be reasons to hard code a constant into a call to srand()

but none of them apply in this case

As long as we're being precise, the quote is "C for about 30", rather than "about 30 years". Of course, since I haven't used C each waking moment of those 30 years, one could argue ... if one were being a ...

The old V6 C compiler allowed pointers and integers to be used interchangably without warnings

"You will not be able to reproduce your experiment exactly, unless you remember what seeds you used. ... Unless this can be formally proven a priori (as Dave Thomas has, in the case of Cordova's program), it's begging the question. It's a good hint if in 10 runs the answer is always the same, but this is not strictly proof that the search is targeted. Cf. the Halting Problem; maybe something different will happen on the tenth run. Furthermore, (and this is really problematic for the counterarguments against ID) if the answers are different, it's insufficient to conclude that the search is not targeted. ... Secondly, how did you tell that a solution was reached?"

I doubt this is a serious pitfall. When we do experiments elsewhere we allow for some uncertainty. The main point must be that according to a reasonable measure, the search will end up close to a solution in enough number of cases.

"Very nice! Is the Windows version of the program availaible?"

Windows?

Is there a Turing test for operating systems?

I think he's saying that a fitness function which values phenotypic closeness to the optimum can be considered "targeted". That is, a function f:C->[0,1] is targeted if and only if f© increases when c gets closer to c_o, the optimum. But that's tautologically equivalent to saying that f is continuous and has exactly one optimum. So your experiment is a nice refutation, having multiple optima (McGuyvers) and lots of continuity breaks.

It occurred to me that I should mention that the basic idea of the Design Challenge was originally suggested by Marek 14, who wrote way back on July 7th Comment #110272 Posted by Marek 14 on July 6, 2006 02:27 AM A suggestion: how about running the simulation again, this time with a more complex set of points where Steiner solution is neither as symmetrical, neither actually known in advance? In that case, front-loading the solution is not just a silly idea - it's actually impossible. Great idea! Thanks, Dave

— Dave Thomas

I doubt this is a serious pitfall. When we do experiments elsewhere we allow for some uncertainty. The main point must be that according to a reasonable measure, the search will end up close to a solution in enough number of cases.

As I've argued repeatedly, if we knew the that much about the landscapes my company's GAs evolve on, we wouldn't bother to use a GA: we'd just write down the damned optimum phenotype and be done with it.

... knowing how to calculate whether one individual is fitter than another provides no more information about the topography of a fitness landscape than is available to biological populations, namely information about the shape of the volume of the landscape that is immediately occupied by the population.

I've been meaning to relearn C/C++ for a while now. I also have an interesting idea for making the evolutionary algorithm even less "targeted". A match made in programmer heaven!

When I have a moment, therefore, I will be rigging the GA to work via the expedient of a very simple predator/prey system, whereby the prey seek out food sources (nodes) whilst being "eaten" by carnivores - lines that'll be randomly dropped on the surface. That will mean that the fitness function is implicit, rather than explicit, which will more closely mirror meatspace evolution.

I predict that this system will also be able to locate both solutions and MacGuyvers, thus demonstrating once again that even the least explicit fitness function can generate high-CSI results - as long as one chooses one's specification so that it matches the fitness criteria.

Extending Marek 14's suggestion of forbidden zones, why not assign a cost to each point of the space, so that forbidden zones have infinite cost, but more generally, some parts are more expensive than others?

...but more generally, some parts are more expensive than others?

GuyeFaux --- Not my plan. Straight lines are still straight lines. But rather than the cost being the Euclidean distance, the cost is the sum of the weights along the line.

Ok,
I've finally finished the whole darn set of posts, counter-posts and comments. Not being an engineer seems to really hamper my ability to process. (My schooling taught me to be a heck of a stamp collector though. And, unfortunately I haven't put any of that resource into putting the phylogenetic tree in the proper order so I have turned out pretty useles for a lot of things)

WHat I am left with is a basic question. Are we basically talking about the math behind a generic emergent system? You are talking about fitness functions and so forth and UD claims those are the result of a designer. No? But that is not even close to the case if I read this correctly. A fitness function is simply the outcome that you would expect given the parameters of what is. No? For example: a slime mold tracks pheremones. The little single cell guy operates on a few simple rules. He emits pheremone x when he encounters situation y and reacts in z fashion when he encounters pheremone x. With a potential pheremone outut of 6 or 8 different pheremones, his options are statistically limited. Eventually he will agglomerate given the correct stimuli. Or not, given different stimuli.

You could most likely write a software program that could replicate this exactly. (I thought that this is basically what SIM City does). And the Intelligent agent would be putting in "fitness functions" that were simply the range of triggers in the environment. Not that they invented them so much as they recognized them. The fitness function then, doesn't it merely describe what the environment is? The emergent behavior, like a Mandelbrot set, would emerge due to it's natural constraints. The intelligent designer (software guy) is merely replicating what is not what they would like to see. Am I way off?

Straight lines are still straight lines. But rather than the cost being the Euclidean distance, the cost is the sum of the weights along the line.

Problem with this is that it's kind of annoying to figure out what a "straight line" connecting two points is. More annoyingly, figuring out the length of a line segment in distorted space is a bit time consuming.

GuyeFaux --- Precisely. One could even make it more exotic by making each intermediate joining point cost something to build...

BWE --- No, you are way on! A slight change is that the fitness function describes how adapted the individual is to the environment.

BWE, I've got no idea what you just said.

To clarify, a fitness function is a proxy for the environment. That is, it determines which critter gets to reproduce. Given a fitness function f and two critters X and Y,
f(X) > f(Y)
means that the GA will make sure that X has more children than Y.

In meat-space, the fitness function is the environment; it's the thing that selects one critter over the other. It reflects the preferential survival/reproduction capabilities of critters.

(Actually, in nature critters aren't preferentially selected; genes are. But the critter is a decent proxy for it's genes.)

Like whether the emergent behavior would not emerge because the environment wouldn't support it? So you have some of the variables stop certain outcomes? Is that even a variable or simply a formula that says If (emergent behavior)=(something that doesn't work) then goto line 10?

Where (something that doesn't work) is a set of parameters describe the physical world?

Posted by GuyeFaux on September 3, 2006 03:33 PM (e) BWE, I've got no idea what you just said. To clarify, a fitness function is a proxy for the environment. That is, it determines which critter gets to reproduce. Given a fitness function f and two critters X and Y, f(X) > f(Y) means that the GA will make sure that X has more children than Y. In meat-space, the fitness function is the environment; it's the thing that selects one critter over the other. It reflects the preferential survival/reproduction capabilities of critters. (Actually, in nature critters aren't preferentially selected; genes are. But the critter is a decent proxy for it's genes.)

GuyeFaux --- Not my plan. Straight lines are still straight lines. But rather than the cost being the Euclidean distance, the cost is the sum of the weights along the line.

Where the emergent behavior is speciation.

I mean, they aren't making them up from what they "want" it to look like.

Right? So your fitness function is essentially determined by the parameters of the niche or environment.

Adding even the most trivial bumps to the surface makes the optimal solution stupidly hard to compute by hand. However, the fitness function need not suffer too much computationally.

So I like David B.B.'s idea.

I thought that the same type of challange could be issued with the traveling salesperson problem as well: "Here's a matrix of distances, what's the shortest circuit?" But unfortunately there are many known algorithms out there to solve the problem, so Sal et al could use them to "look in the back of the book", as he's done for this problem. But adding a small novelty would take care of that issue...

The fitness function then, doesn't it merely describe what the environment is?

Popper's ghost & GuyeFoxx --- Right on. This essentially eliminates any attempt to use symmetries to arrive at even local minima. So one is reduced to brute force, which GAs are good at, along with other satisficing algorithms such as 'computational swarm intelligence' and 'simulated annealing'.

Actually, I don't know if this experiment counts as a demonstration of speciation. Critters changed to suit their environments, but it's not clear to me if we could identify a species from which two species arose. Someone correct me here.

The demarcation of species in the absence of sexual reproduction seems to be entirely arbitrary.

During the process, occasional mutations, and sex (via crossovers) are employed in every generation.

I wrote some GA code to optimize natural gas flows around 6 years ago. I used sexual reproduction and the occasional run developed into more than one species. That is more than one optimal solution appeared simultaneously in the population. When there was mating across the species the poor child was sub-optimal and usually didn't survive selection.

However, the situation was not permanent as the species that was slightly more optimal eventually crowded out the second species.

I enjoyed writing the GA code but my managers didn't like the fact that the answer always came out slightly different on each run.

Michael

I have been thinking that instead of evaluating individuals one-by-one, we should pass the entire population to a procedure that assigns relative, not absolute, fitness values to individuals. This might make things more clear for the IC (Irremediably Concrete).

JP>Your fitness function doesn't determine survival by ranking JP>closenessof fit to the goal (shortest path length)? That's right, it does not. Outside of pedagogic exercises like WEASEL, the fitness function does not have the information of what the correct solution is (which would be a distant ideal target), or what the minimum closed path length is (which is the metric which would be found when applied to a distant ideal target). What the fitness function does is provide a metric by which the *current* candidates can be ranked.

I've read this series with ever increasing interest.
I'm really fascinated by the Macgyvers (great name btw).
Not quite perfect, but good enough to survive in the given environment.
...which is nice.

You're right, in light of the tautoligies expressed by Dembski et al (quoted by Dave Thomas) telling us what "targeted" means: valuing "proximity to the optimum" as opposed to "valuing the current function." However, they should've come up with a better definition of "targeted"; something about how the fitness function is expressed. Seems to me like the IDers don't want to treat the fitness function as a black box, since they keep insisting that the programmer "smuggles in" information.

Why is Dembski worried about a machine producing a result that is analogous to the "set theoretic compliment of chance and regularity" .....in other words a freak accident of interacting natural forces and, or matter.

Would that make his arguments seem like Dawkins is correct?

WAD here is a new book title for you.

"The Selfish Algorithm".....has a nice ring don't you think?

Of course it's not too original...and it might make you look as if you are selling out..

...but hey.... you could call it "The Co-operative Design Algorithm"

...ah typed out on god's trusty old "Imperial Typewriter" and flicked off to the great halls of angels where it was mimeographed using freshly plucked quills and then sent the great smoking DNA factories in the skies before shipping to each corner of the universe by cosmic vacuum gravity pumps.

Just an idea.

I'd be happy to supply him with one...

Actually, I think it is Dave who doesn't want a black box fitness function. I'd be happy to supply him with one, but he is smart enough to know, and indeed experimented with a couple of alternatives, that reaching results consistent with his stated purposes, requires careful selection of the fitness function. "Valuing the current function" is closely aligned with "proximity to the optimum".

Actually, I think it is Dave who doesn't want a black box fitness function. I'd be happy to supply him with one, but he is smart enough to know, and indeed experimented with a couple of alternatives, that reaching results consistent with his stated purposes, requires careful selection of the fitness function. "Valuing the current function" is closely aligned with "proximity to the optimum".

Ok, I will try again.

The ID group (Sal et al) says that the fitness function is (or is the result of) an Intellegent Designer. No?

But the fitness function is simply a set of parameters imposed by the natural surroundings, right?

Even though you wrote it as software, isn't the point of the function to mimic the limits imposed by nature? The intelligent designer isn't the one who makes CO2 heavier than O2 or N2. So the fitness function, which is seeking to replicate fitness as regards the ability to be successful in a given environment and assign it a value on a scale (right?) isn't god putting out his finger and saying poof, it is the ability to utilize or function within existing parameters. Right?

No I-Designer, right? Is this more complicated than that? Not the software, the concept of the fitness function being, in essense, a designer. The fitness function merely imposes parameters?

Emergent systems all emerge by applying a basic set of rules within a fixed set of parameters, from fractal geometry, the golden mean, ant colonies and slime molds to evolution of the species. No?

Ok, I will try again. The ID group (Sal et al) says that the fitness function is (or is the result of) an Intellegent Designer. No?

But the fitness function is simply a set of parameters imposed by the natural surroundings, right?

Even though you wrote it as software, isn't the point of the function to mimic the limits imposed by nature? The intelligent designer isn't the one who makes CO2 heavier than O2 or N2.

So the fitness function, which is seeking to replicate fitness as regards the ability to be successful in a given environment and assign it a value on a scale (right?) isn't god putting out his finger and saying poof, it is the ability to utilize or function within existing parameters. Righ?

No I-Designer, right? Is this more complicated than that? Not the software, the concept of the fitness function being, in essense, a designer. The fitness function merely imposes parameters?

Emergent systems all emerge by applying a basic set of rules within a fixed set of parameters, from fractal geometry, the golden mean, ant colonies and slime molds to evolution of the species. No?

Ok, I will try again.

The ID group (Sal et al) says that the fitness function is (or is the result of) an Intellegent Designer. No?

But the fitness function is simply a set of parameters imposed by the natural surroundings, right?

Even though you wrote it as software, isn't the point of the function to mimic the limits imposed by nature? The intelligent designer isn't the one who makes CO2 heavier than O2 or N2.

So the fitness function, which is seeking to replicate fitness as regards the ability to be successful in a given environment and assign it a value on a scale (right?) isn't god putting out his finger and saying poof, it is the ability to utilize or function within existing parameters. Righ?

No I-Designer, right? Is this more complicated than that? Not the software, the concept of the fitness function being, in essense, a designer. The fitness function merely imposes parameters?

Emergent systems all emerge by applying a basic set of rules within a fixed set of parameters, from fractal geometry, the golden mean, ant colonies and slime molds to evolution of the species. No?

The book "Simple Genetic Algorithm" is quite good for the mathematically minded. But in particular, the author offers at least three different ways in which selection of the 'best' to have children might be accomplished. So-called elitism is one of these.

Can you suggest something? I'm curious what you think an "information-free" fitness function is.

For example, if I had specified "longer is better" in my Genetic Algorithm - or not considered length at all - I would have not ended up with compact structures. (I know - I did the experiments!)

Of course, thousands upon thousands of different "fitness tests" could be conceived. - Dave Thomas

I don't think Dave really cares about Steiner solutions in specfic, though. Dave's "stated purposes" with these articles are to demonstrate that his genetic algorithm can find the optimum for some fitness function.

Now, if you have an intelligent designer getting you past that hump...

No, it isn't. "Proximity to the optimum" requires prior knowledge of the optimum. That's not how real fitness functions work. They work by comparing the relative performance of current candidate solutions.

I don't think Dave really cares about Steiner solutions in specfic, though. Dave's "stated purposes" with these articles are to demonstrate that his genetic algorithm can find the optimum for some fitness function.

Roger Rabbit: Of course, thousands upon thousands of different "fitness tests" could be conceived. - Dave Thomas

Maybe I am misreading this. What exactly is the objection? THat the ID'er is the fitness function? Not the way I read it. It is merely an emergent system's constraints. Where am I missing something?

...fitness function selection was based on prior knowledge about the optimum being sought.

I think the ID hairsplitting is getting a bit silly. As has been repeatedly said in the real world the fitness function is not in the cell but is the environment.

So in a desert the fitness function is to survive well enough to reproduce in desert conditions. Where is the Intelligent Designer in this scenario?
Behind which Cacti are the solutions hidden? So creatures better able to manage heat and lack of water do better, reproduce more and survive.

I really can't see the difference between this and an environment which says shorter is better. The creatures that are shorter are aloud to reproduce at the expense of the longer length solutions.

Re "You've missed the moving goalpost."

Which is probably good, since actually hitting the moving goalpost might hurt.

Henry

I think the ID hairsplitting is getting a bit silly.

... in the real world the fitness function is not in the cell but is the environment.

I don't think I said anything about "information free" fitness functions, but as for alternatives, I suggest one, which Dave admits he tried:

For example, if I had specified "longer is better" in my Genetic Algorithm - or not considered length at all - I would have not ended up with compact structures. (I know - I did the experiments!)

— Roger Rabbitt

So, something important is getting the right FF.

I won't debate what are the characteristics of "real fitness functions". But it is clear, and admitted by most folks here, at least tacitly if not explicitly, that the fitness function selection was based on prior knowledge about the optimum being sought.

But it does stand in stark contrast to how you are trying to paint it.

I'm not sure why some here seem to be under the impression that IDers don't think FF's designed to find their optimum generally can't. I think they generally can.

I won't debate what are the characteristics of "real fitness functions".

But it is clear, and admitted by most folks here, at least tacitly if not explicitly, that the fitness function selection was based on prior knowledge about the optimum being sought.

Yes it functions by comparing "current candidates", but on what basis do we select for "relative performance"? That's determined by the intelligently designed FF because of how it relates to the goal Dave honestly admitted to.

There isn't anything illegal or immoral about that. But it does stand in stark contrast to how you are trying to paint it.

No, I think it is many orders of magnitude more than thousands upon thousands. Now, if you have an intelligent designer getting you past that hump...

— Roger Rabbitt

Dave's exercise makes it clear that GAs can optimize FFs (without coding the specific solution into the FF). Since they can, a claim that they can't is false. Therefore, the ID claim that evolution cannot produce organisms highly fit to their environment is false.

— Popper's ghost

Nevertheless, as shown elsewhere (Meyer 1998:127-128, 2003:247-248), these [genetic algorithm] programs only succeed by the illicit expedient of providing the computer with a "target sequence" and then treating relatively greater proximity to future function (i.e., the target sequence), not actual present function, as a selection criterion.

— Meyer

Dave's exercise makes it clear that GAs can optimize FFs (without coding the specific solution into the FF). Since they can, a claim that they can't is false. Therefore, the ID claim that evolution cannot produce organisms highly fit to their environment is false.

— Popper's ghost

Speaking precisely, your "therefore" which I bolded is unwarranted.

Actually no; "therefore" in this case means "this is sufficient to show that". (At least that's the meaning in math papers.) I think you're interpreting "therefore" as saying "this is sufficient and necessary to show that". Which, as you've pointed out, is not the case.

— GuyeFaux

Dave's exercise makes it clear that GAs can optimize FFs (without coding the specific solution into the FF). Since they can, a claim that they can't is false. Therefore, the ID claim that evolution cannot produce organisms highly fit to their environment is false.

— William E Emba

Speaking precisely, your "therefore" which I bolded is unwarranted. What can be deduced from the success of Dave's exercise (and that of countless other GAs that have been run over the decades) is the ID argument about the alleged improbability of real world evolution is invalid.

"Valuing the current function" is closely aligned with "proximity to the optimum,"

This [Meyer's] may be a somewhat reasonable complaint: They [IDists] are arguing that not all intermediates posses selectable traits.

What I'm asking is for critics to pinpoint the code snippets where "proximity to the optimum" is "hardwired in," especially for the 4-point, 5-point and 6-point systems discussed in this series.

In response to Dave, I think you missed my second posting.

While I am not an IDist I think I can see their point of view (warped though it is )that the nature of the selectivity is the problem.

They see this kind of algorithm as a clear chain of A-B-C-D where each step is closer to a solution and therefore more selectable. It does not matter that the solution itself is not hardcoded only that the fitness function evaluates in terms of proximity to any solution.

Perhaps if I phrase it as:
"Valuing the current function" is closely aligned with "proximity to an optimum,"

It seems like this is a ridiculous arguement that the model is invalid because we can look at two species and tell which one is more fit, as though to say selectability itself must be denied. At the same time it is accurate that in a more realistic model most mutations would have to be treated as neutral and it may take several successive mutations before a change in fitness could be determined.

I do feel the onus is on ID to open discussion and propose their own models or variations on this model which can present a selectability and fitness model which is not strictly progressive, is not considered a "targeted" approach, but must still able to identify stages where a given variant is superior.

I doubt that they will ever engage in such a discussion since they will likely realize that they are essentially defining "selectable" and "targeted" as the same thing. It is an intentional inability to grasp that an artificial selection model when supplied arbitrary or random selection criteria is accurately considered a representation of a natural selection model.

As someone said earlier all of this can be considered models of so called "micro" evolution, which most IDist are not crazy enough to dispute. Of course from a real scientific standpoint there is no reason to believe there is any significant difference between "micro" and "macro" evolution, in fact real scientists have little cause to even use such terms. In analogy gravity works equally on pebbles and boulders, so why should evolution be any different?

I don't think most IDers believe that evolution doesn't work _at all_. Reading this IDEA Center FAQ article it sounds like microevolution is acceptable, and as I said in a previous comment what most function optimizing EC does (even with niches) looks like microevolution.

— David vun Kannon

Speaking precisely, your "therefore" which I bolded is unwarranted.

Another way I could have put it is "Therefore, the ID claim that evolution cannot produce organisms highly fit to their environment for the reasons they give is false." I think that's what I had in mind.

Another way, William, I might have put it is Dave Thomas's "publicly admit that evolutionary processes like selection, reproduction and mutation can indeed produce highly coherent, irreducibly complex, and even complexly specified designs, without having to know anything about the specifics of these designs". I suppose that, despite all that, it is conceivable that evolution cannot produce organisms highly fit to their environment. When making empirical claims, it's always risky to use deductive words like "therefore". But it is reasonable to interpret a denial of a claim that something cannot happen as a denial that it cannot happen for the claimed reasons, not in an absolute sense. Still, the clarification has some value.

In biological evolution the fitness function does not select on the basis of a particular property of the evolving population, but rather selects on the basis of any property that increases reproductive success of the phenotype; it is indifferent to which property that might be.

GA: F = f(p1) BE: F = g(p1,p2,p3,.., pn)

I think the "fine tuning" arguments of the IDists reduce to ignoring this difference.

What about a FF that wasn't designed to find its optimum ... won't it find its optimum anyway? If not, why not?

these [genetic algorithm] programs only succeed by the illicit expedient of providing the computer with a "target sequence" and then treating relatively greater proximity to future function (i.e., the target sequence), not actual present function, as a selection criterion.

— Alann

This may be a somewhat reasonable complaint:

In the simplest model say fitness equates to distance from a arbitrary point (0,0). In the simple approach normally taken this means (5,0) is more fit than (10,0).

It does not matter that the solution itself is not hardcoded only that the fitness function evaluates in terms of proximity to any solution.

No, the FF does not give distances from the solution. In the case of the Steiner Tree, the length of the path is not the solution, it's just a property of the solution.

Yes, smaller segment sums are favored because the goal is to minimize the sum of the segments --- of course. Yes, Dave "honestly admitted" that he's trying to produce Steiner Trees, which are defined as having a minimal sum of segments --- of course.

If you wish to continue claiming that

"Valuing the current function" is closely aligned with "proximity to the optimum,"

then simply find and describe this close alignment in the publicly posted listing for the GA.

Are you not seeking to investigate Steiner's trees, as you previously claimed? Are Steiner trees not defined as having the shortest length? Are you not selecting based on "shorter is better"? You seem to be hung up on whether the specific answer (for example, the minimal length) is known in advance to either the programmer or the program. But neither is necessary to intelligently program the selection function. I can use a computer program to calculate 25 to the 25th power, even though neither I nor the program know the answer in advance. I'm not surprised by the results, and I don't know any IDers who are.

— Mr. Anonymous Toon Character

Nevertheless, as shown elsewhere (Meyer 1998:127-128, 2003:247-248), these [genetic algorithm] programs only succeed by the illicit expedient of providing the computer with a "target sequence" and then treating relatively greater proximity to future function (i.e., the target sequence), not actual present function, as a selection criterion.

I'm confused.

Even your allies admit, Dave, that this is what you are doing.

Are Steiner trees not defined as having the shortest length?

You seem to be hung up on whether the specific answer (for example, the minimal length) is known in advance to either the programmer or the program.

But neither is necessary to intelligently program the selection function.

I can use a computer program to calculate 25 to the 25th power, even though neither I nor the program know the answer in advance. I'm not surprised by the results, and I don't know any IDers who are.

Numerous folks are trying to make the claim that this demonstrates the ability of Darwinian evolution in the biological world, but I remain a skeptic.

Darwinian evolution operates on Natural Selection. Your GA operates on intelligent selection.

Now, if you can demonstrate to me that NS can do anything an intelligent selector can do, then you might be able to close the circle.

What you seem to be missing is that I'm simply testing the current population to find "shorter is better" candidates for breeding the next generation, and not illicitly providing the computer with a "target sequence" (e.g. the formal Steiner solution(s)), nor am I testing digital organisms for "relatively greater proximity to future function (i.e., the target sequence)."

— Dave Thomas

Roger Rabbitt wrote:

"Your GA operates on intelligent selection."

How? Dave has pointed out that neither he or the computer knew the answer. (I hope you're not also suggesting the computer itself is intelligent.) The computer also continued to find more solutions after it already found one. I'll admit, the mathematics of this stuff is over my head, but the principle makes sense. The FF represents the natural environment and the GA adapts to it (is that right?). And with all these people using their own GA's to come up with many different solutions (to which they didn't know the solution either until their program found it), where is the intelligent selection?

As far as I can tell, the FF represents a problem to be solved and we use the computer to help us solve it. The only thing I can think of that's selected would be the FF itself, but what difference would that make? If it represents the environment, what difference would that make to natural selection if the environment occured naturally or was "Intelligently Designed"?

If I've misunderstood anything (probably have) and deserve a verbal slaughtering, then feel free. :-)

In the meantime I'll just say: "Welease Woger!"

When making empirical claims, it's always risky to use deductive words like "therefore".

— Popper's ghost

But it is reasonable to interpret a denial of a claim that something cannot happen as a denial that it cannot happen for the claimed reasons, not in an absolute sense. Still, the clarification has some value.

Specifically, the argument from "A => B" to "not-A => not-B" has no logical validity

but in a scientific context it can be quite significant

To be a bit more specific: there's an argument that any genetic algorithm must be "front-loaded" via "illicit expedient" with "the target sequence" in order to reach the target. If this argument were valid, then evolution (A) must be front-loaded. But the existence of a GA (B) that isn't front-loaded yet reaches its target demonstrates that the argument is invalid, and thus the claim that evolution must be front-loaded is false (but perhaps it is front-loaded anyway). This gets back to GuyeFaux's comment about necessary vs. sufficient.

It's depressing to think we still don't really know how to answer Hume.

I should add that Occam's Razor is not merely a methodological rule but is actually true when phrased something like: A simpler theory is more likely to make accurate predictions, since adding extraneous elements increases the number of predictions without making the theory fit the evidence any better. So in a sense, your namesake answered Hume.

I enjoyed writing the GA code but my managers didn't like the fact that the answer always came out slightly different on each run.

No, Meyer's statement is just flat wrong; there is no "target sequence" in the computer anywhere (except in cases like Dawkins' weasel) until it stumbles upon it.

— Popper's ghost

I'm confused. Even your allies admit, Dave, that this is what you are doing. So, why are you demanding that I produce the code for you?

— Roger Rabbitt

"Darwinian evolution operates on Natural Selection. Your GA operates on intelligent selection."

ID is attempting to define "target sequence" in a broader sense which is seems to be selection itself. This may be a reasonable view if an only if they can provide a good answer to a hard question: Under the ID definition how can you model select-ability in a manner which would not be considered a targeted sequence?

In my opinion Meyers is arguing that since the problem has a solution and the fitness function can tell if one variation is a better solution than another it is a "target sequence".

For example in the sequence 1,2,3,...n; although n is never specified in any real sense the fitness function identifies that 3 has better survivability than 2 and would theoretically recognize the value n if reached as the most survivable. There is so much wrong with this. The fitness function doesn't "recognize" anything, and certainly doesn't "recognize" the optimum; it's simply a comparison. If 3 is preferred over 2, then 3 is preferred over 2. But there is no progression from 2 to 3. In the Steiner network problem, the algorithm does not find a network of length m, and then turn it into a network of length m-1, and so on, until it reaches the optimum. That just ain't how it, or evolution, works. If you are referring to me, you are definitely confused.

In my opinion Meyers is arguing that since the problem has a solution and the fitness function can tell if one variation is a better solution than another it is a "target sequence".

For example in the sequence 1,2,3,...n; although n is never specified in any real sense the fitness function identifies that 3 has better survivability than 2 and would theoretically recognize the value n if reached as the most survivable.

If you are referring to me, you are definitely confused.

BWE said:

"Fish don't know to school. They have a limited number of responses to the fish next them. Schooling is an emergent behavior of the system. The specific school pattern includes environmental constraints/inputs (a fitness function) but the generic "schooling" behavior is simply the result of an algorithm of the various cues that the schooling fish respond to."

I've seen a documentary showing that. It also showed birds too, operating on the exact same principle. Another thing they also did was do a computer simulation. All of the "birds" on screen were represented by coloured dots. All of the dots were given the same specific parameters of motion - two or three simple instructions (follow the dots nearest them, don't get too close or too far). Then the simulation was run. We saw a few hundred dots all flying around the screen moving exactly like a flock of birds, with some footage of real birds for comparison. It was pretty cool.

What was it called? I'd like to send it to someone.

I too remember seeing that video a while back.

ITMT, you might get a kick out of this BWE:

http://www.humboldt.edu/~ecomodel/clupeoids.htm

ahh, and speaking of schooling being an emergent property, I did remember the name Parish, and found a more recent article than the ones I recalled:

http://www.biolbull.org/cgi/content/full/202/3/296

..and finally, this is where you can play with the basics of the models yourself, IIRC:

http://depts.washington.edu/~birdfish/schooling/model.shtml

STJ, I've seen that stuff many, many times but it is always good to repeat it. And it is especially good for a step by step process of enquiry that might lead a fundy straight to heck. Where all the good people go. :-)

STJ, I've seen that stuff many, many times but it is always good to repeat it. And it is especially good for a step by step process of enquiry that might lead a fundy straight to heck. Where all the good people go. :-)

In fact, way back before we knew to call them "Emergent Systems" I had a fancy for some particularly peculiarly prickly starfish that could tear your ass bad if you sat on them. Seems that populations of the prickly bastards could fluctuate rather drastically

I had a fancy for some particularly peculiarly prickly starfish that could tear your ass bad if you sat on them. Seems that populations of the prickly bastards could fluctuate rather drastically...

Sorry, BWE, it was a few years ago. I wish I could remember for the life of me. We only had terrestrial TV back then so I'm guessing it was on BBC2's "Horizon" programme, or on Channel 4 maybe. If I do manage to find out, I'll certainly let you know. Sorry 'bout that. :-(

If anyone here is a physicicst, and knows GA's, could you drop me an email at rkhull@roanoke.edu?

I've looked around and I can't find it. My wife teaches middle school science and she could use something like it.

Minions of Christ, do you get why the FF isn't an Intelligent agency? I don't understand all too well what it is you mean by the idea that you are pre-selecting for the answer. Maybe Roger Rabbit can spell it out. This seems too important to let it lie.

Do you agree that the software is demonstrating that evolution is possible?
Why or why not?

Not too which person you're responding to there, but I get that the FF isn't intelligent and the software IS demonstrating that evolution is possible. Far as I can tell, only the Rabbitt has that problem. Probably the reason why we don't usually let rabbits near computers. :)

What you seem to be missing is that I'm simply testing the current population to find "shorter is better" candidates for breeding the next generation,

and not illicitly providing the computer with a "target sequence" (e.g. the formal Steiner solution(s)),

nor am I testing digital organisms for "relatively greater proximity to future function (i.e., the target sequence)."

I have amply demonstrated that my program selects for present function, not future function.

Life isn't like that. Evolution has no long-term goal. There is no long-distance target, no final perfection to serve as a criterion for selection, All appearances to the contrary, the only watchmaker in nature is the blind forces of physics, albeit deployed in a very special way. A true watchmaker has foresight: he designs his cogs and springs, and plans their interconnections, with a future purpose in his mind's eye. Natural selection, the blind, unconscious, automatic process which Darwin discovered, and which we now know is the explanation for the existence and apparently purposeful form of all life, has no purpose in mind. It has no mind and no mind's eye. It does not plan for the future. It has no vision, no foresight, no sight at all. If it can be said to play the role of watchmaker in nature, it is the blind watchmaker.

What about a FF that wasn't designed to find its optimum ... won't it find its optimum anyway? If not, why not?

Despite claims to the contrary, you did have prior knowledge about the solution

Allow me to quote Dawkins:

"Proximity to the optimum" requires prior knowledge of the optimum. That's not how real fitness functions work. They work by comparing the relative performance of current candidate solutions. - Wesley R. Elsberry

Ok, I vote stupid, since the statement RR just quoted goes against him.

Specifically, RR is too stupid to grasp that "requires prior knowledge of the optimum" means "requires prior knowledge of what the optimum is", not "requires prior knowledge about the optimum".

Extremely stupid bunny.

OTOH, perhaps RR does know that ""requires prior knowledge of the optimum" means "knows the optimum ahead of time" and is just pretending not to. After all, just how stupid can someone be?

BTW, Wesley made it explicit in the very post from which RR mined that quote: "You don't have to know what the actual minimal tour length is in order to deploy it and get a short tour as the result of running the algorithm."

"Ok, I vote stupid, since the statement RR just quoted goes against him."

That's what I thought.

" "Proximity to the optimum" requires prior knowledge of the optimum. That's NOT how real fitness functions work." [emphasis mine]

Lettuce, anyone?

Life isn't like that. Evolution has no long-term goal. There is no long-distance target, no final perfection to serve as a criterion for selection

In all GAs I know of, including Dave's, the fitness function selects on the basis of a particular property of the genotype or phenotype. The fitness function is univariate (though one can complicate that with 'soft' contraints'). In Dave's program, that property is the phenotypic length of path, with short paths being favored. In biological evolution the fitness function does not select on the basis of a particular property of the evolving population, but rather selects on the basis of any property that increases reproductive success of the phenotype; it is indifferent to which property that might be. A population in a region of growing aridity can increase its fitness over time by evolving ways of conserving water, and/or evolving ways of extracting water from the environment, and/or evolving ways of more efficiently utilizing water, and/or something else. In other words, GA: F = f(p1) BE: F = g(p1,p2,p3,.., pn) Where F is the selective fitness (reproductive advantage) and p is some property/trait of the phenotype. I think the "fine tuning" arguments of the IDists reduce to ignoring this difference. In GAs we "fine tune" the fitness function to evolve solution(s) to a problem that we have defined as interesting. In biological evolution there is no such fine tuning. Biological evolution grabs whatever property(ies) thrown up by the variation-generating mechanisms that provide an immediate gain in reproductive advantage in a given environment. Variations in multiple properties are on offer, and natural selection picks and chooses among them.

— RBH

Silly Rabbit, ID is for Maroons. And you're proving that over and over and over. Take 3 steps back, forget the equations and realize that the entire argument is wrong. You have missed the whole point or you are deliberately obfuscating and you know your arguments to be wrong.

The Toon quotemines Dawkins:

What about a FF that wasn't designed to find its optimum ... won't it find its optimum anyway? If not, why not?

The confusion has already been addressed in these comments, by Richard Hoppe.

For example in the sequence 1,2,3,...n; although n is never specified in any real sense the fitness function identifies that 3 has better survivability than 2 and would theoretically recognize the value n if reached as the most survivable.

There is so much wrong with this. The fitness function doesn't "recognize" anything, and certainly doesn't "recognize" the optimum; it's simply a comparison. If 3 is preferred over 2, then 3 is preferred over 2. But there is no progression from 2 to 3. In the Steiner network problem, the algorithm does not find a network of length m, and then turn it into a network of length m-1, and so on, until it reaches the optimum. And if it does reach the optimum, it doesn't stop; it has no way of knowing that it is an optimum.

— Popper's ghost

It is a fact that there is a value or values of x which result in the highest possible result for f(x).

In this sense the fitness function does "recognize" an optimum by returning the highest possible result.

There is progression here. Using a simple representation of mutation +/-1 it is equally likely for 2 to become 1 or 3.

This is a silly complaint anyway since evolution predicts a progression towards fitness.

Lets alter the example slightly: The fitness function calculates such that numbers with the fewest number of integer factors have the best survivability. As an observer we realize that prime numbers have the best survivability in this environment.

This seems to me a reasonable representation of so called Irreducible Complexity.

IC may have a sound theoretical basis

It is the process which does not recognize an optimum and will continue to produce new variations even after an observer identifies that the optimum has been reached.

I think you are trying to dismiss my point out unfairly.

In a real world sense natural selection does recognize an optimized variant, this does not in any way imply that natural selection or any other fitness model is inately an intelligent process. We can and have engineered species like disease resistant crops which have better survivability then the purely natural variants.

I never said mutations act on the fitness function. The range of mutation is limited but the result does not favor fitness. That is why I said with a range of +/-1 for mutation 1 and 3 are equally likely offspring of 2. It is the probability of 3 having offspring vs 2 having offspring which is favored independent of mutation.

I specifically restated my example which I realize is differnt than the stienr. I am trying to express the principles as simply as possible but it applies equally to the Stiener problem:

What ID is calling the "target sequence" (and yes I agree this is bad termonology) is the idea that the fitness equation has an optimal solution and in the conceivable lineage there are no necessary steps which are neutral or less fit.

They do miss the idea that the Stiener solution could model Irreductible Complexity as well. In principle there exists several possible initial states which will almost invariable favor mutation into McGuyver solution. Once a McGuyver variant exists selection will strongly favor the McGuyver over the steps needed to reach the double bowtie solution. Put another way one of the steps between the McGuyver and the double bow tie is sufficiently harmful when compared to the existing McGuyver that it will invariably be selected out.

I tried to expressed this as the jump from 23 to 29 where 24 has multiple factors (1,2,3,4,6,8,12,24).

My understanding of IC in biological terms does not require that the intervening step (removed piece) is inherently lethal, only that the value of the selectable trait has been lost, such as a flagella tail which cannot propel.

In principle IC has merit. You can show in principle that a catalyst is required, and is missing from our current understanding of the formula. If you argued IC in chemical terms you could prove it. The problem is IC has little practical value in biology because we are already so many factors which we cannot define precisely that create a margin of error while small in reasonable terms more than sufficient to account for IC.

What ID is calling the "target sequence" (and yes I agree this is bad termonology) is the idea that the fitness equation has an optimal solution

Anyway I am through playing the Designer's Advocate.

Dave's point against inference of design loses none of its potency.

The McGuyver results strongly refutes the suggestion of a true target sequence. Getting stuck with these non-optimal variants is very much in line with evolution and natural selection.

As much as ID wants to complain that the problem is unfair, this places the burden on them to explain what a fair problem would be. Too bad they suck at coming up with alternatives.

Alann wrote:

What ID is calling the "target sequence" (and yes I agree this is bad termonology) is the idea that the fitness equation has an optimal solution and in the conceivable lineage there are no necessary steps which are neutral or less fit.

I think you are trying to dismiss my point out unfairly.

As much as ID wants to complain that the problem is unfair, this places the burden on them to explain what a fair problem would be. Too bad they suck at coming up with alternatives.

What ID is calling the "target sequence" (and yes I agree this is bad termonology) is the idea that the fitness equation has an optimal solution

Nevertheless, as shown elsewhere (Meyer 1998:127-128, 2003:247-248), these [genetic algorithm] programs only succeed by the illicit expedient of providing the computer with a "target sequence"´ and then treating relatively greater proximity to future function (i.e., the target sequence), not actual present function, as a selection criterion.

You can't just take an arbitrary Steiner network and mutate it such that it now has a shorter path length

— Popper's ghost

As for future vs. present function this is a philosophical agrument about non-selectable intermediates. In the lineage A->B->C where the fitness of C is greater than the fitness of A, the issue is what the fitness of B should be. If you believe that B is in reality a detrimental step; however the abstract problem uses a fitness model where B is a positive step, can you not argue that the reason B is overrated is because you have created a model where future function is innately expressed in present function? Although to be accurate this is in the abstraction of the problem, not in the fitness model.

— Alann

All right now you're not making any sense. When the model mutates the Stiener path a shorter path must be a possible outcome; otherwise you've reached an evolutionary dead-end.

I was attempting to abstract a general issue from the complexity of the problem. I realize this is not working.

My point was that the argument about future fitness vs. present fitness can be taken to refer to the nature of the problem (that the problem lends itself to functional intermediates) and not as a direct argument against the fitness function.

I do not agree with the ID argument on this point, I am only trying to see their point of view. I apologize for letting this take the conversation so far off topic.

Oh and for the record (since someone seems to think I am and idiot) my background is mathematics, logic, and computer science. I do not consider myself in any way an expert on biology.

Odd then that you show no understanding of mathematics, logic, computer science, or biology.

Comments are now Closed.

It's been fun. Let's do it again some day, shall we?

Thanks & regards, Dave