Yet again, we have:
- An argument from personal incredulity
- God of the gaps reasoning
- A failure to recognise that evolutionary algorithms are being used by engineers to evolve solutions to complex problems that are far more efficient than those created by humans. Random mutation and natural selection have been shown to be able to design complex structures.
The Guided Missile Fire Control System
(GMFCS) Mk 74 functions as part of the missile weapons system to support the anti-air and anti-surface warfare missions of
the ship. Upon receipt of target position data, the GMFCS acquires tracks and
provides accurate target position and rate data for weapon control and
engagement processing. When on target, the fire control system provides orders
to position the launcher for firing, generates preflight orders to the missile,
and provides continuous-wave illumination of the target for missile steering
orders. The fire control system also has the capability to provide horizon
search, sector search, or raster search, as selected by the operator.
Engagement status and repeatback information is used for the weapons system
evaluation.[1]
Pulse-Doppler radar detects both target location in three dimensions as well as radial velocity, allowing it to detect small, fast moving objects that may be obscured by larger, slower objects. As seen above, the military applications are obvious.
It is of course likely that Collyer was trying to argue that science cannot explain how these capabilities evolved, therefore they did not evolve. It this is what he was trying to say, yet again, we have an argument from personal incredulity. Despite not being a scientist, Collyer is using his personal ignorance of many areas of science as evidence that evolution could not explain these facts. As mentioned before, there are unsolved problems in evolutionary biology, but at the risk of repetition, these problems do not overturn the considerable evidence from molecular genetics, palaeontology, comparative anatomy, biogeography and other disparate fields of science that independently confirm the reality of common descent.
The evolution of electroreception in monotremes, echolocation in bats and cetaceans and migration in birds is still an area of ongoing research, but unsolved problems in ecology or physiology do not mean that common descent is false. It does become wearying having to point this out repeatedly to laypeople with an agenda who think they have singlehandedly overthrown the “Darwinian edifice”.
Having said that, it is profitable to realise that rather than throwing their hands in the air and exclaiming ‘It’s a mystery’, scientists set about learning how these abilities could have evolved. Take electroreception in monotremes. It turns out that all three species – the platypus and the two echidnas – have electroreception:
All three extant monotreme species have
electroreception, judged by the presence of mucous gland electroreceptors in
the bill skin. The platypus Ornithorhynchus anatinus,
from East Coast Australian waterways, has 40 000 electroreceptors; the
long-billed echidna Zaglossus bruijnii,
from wet tropical montane forest, has 2000, while the short-billed echidna Tachyglossus aculeatus, widely distributed from alpine areas to
desert, has only 400...The evidence indicates that there has been a reduction
in electroreceptive abilities in the echidnas, with the short-billed echidna
having no more than a remnant of this sensory system. In the dry habitat of the
shortbilled echidna, opportunities for the use of electroreception in prey
capture would be unusual (e.g. during rain). It had been shown behaviourally
that echidnas can identify small electric fields...but it seems reasonable to
conclude that, phylogenetically speaking, this ability is ‘on the way out’ in
the echidna.[2]
Bill sensory organs of the three remaining extant monotreme species, detailing the total number of electroreceptors in the bills of each species (Pettigrew, 1999).
Emily Fong notes:
Platypus are part of the order Monotremata, and all three extant monotreme species have electroreception. These species include the long-billed echidna Zagglossus bruijnii, the short-billed echidna Tachyglossus aculeatus, and the platypus. Of the three, the platypus has 40,000 mucous gland electroreceptors in the bill skin, compared to the 2000 electroreceptors of the long-billed echidna and 400 electroreceptors of the short-billed echidna, and therefore has the most sensitive sense of electroreception (Pettigrew, 1999). Though it has been demonstrated that echidnas are able to sense weak electric fields, this disparity in electroreceptor number suggests the drastic reduction in the electroreceptive abilities in echidnas is the result of evolutionary selection, due to its unsuitability to their environments (Proske et al., 1998). This is particularly true in the case of the short-billed echidna, whose electroreceptive abilities seem to be an artifact, with little applicability to hunting in the dry habitat they live in.[3] (Emphasis mine)
Not all monotremes were created equally it seems when it comes to electroreception. One would have predicted that monotremes in more damp environments would have more electroreceptors, whereas in dry environments, this sensory modality would be less useful from a survival perspective, and not be subject to positive selection.
Getting back to the earlier point
about the alleged inability of man to replicate echolocation, electroreception
and other physiological marvels found in the natural world, it is possible that
special creationists would argue that since a human designer was needed to
create these technologies, a Divine designer was also needed. Of course, this
is the old argument from design that Darwin
destroyed over 150 years ago. Furthermore, it ignores the fact that
increasingly, engineers and scientists are using evolutionary techniques to
produce designs far more elegant than intelligent designers could. Genetic
algorithms and other techniques which form the discipline of evolutionary
computation allow the designer to use evolutionary techniques to simulate
evolution with amazing results:
Concisely stated, a genetic algorithm (or GA
for short) is a programming technique that mimics biological evolution as a
problem-solving strategy. Given a specific problem to solve, the input to the
GA is a set of potential solutions to that problem, encoded in some fashion,
and a metric called a fitness function that allows each candidate to be
quantitatively evaluated. These candidates may be solutions already known to
work, with the aim of the GA being to improve them, but more often they are
generated at random.
The GA then evaluates each candidate according to the fitness function. In a pool of randomly generated candidates, of course, most will not work at all, and these will be deleted. However, purely by chance, a few may hold promise - they may show activity, even if only weak and imperfect activity, toward solving the problem.
These promising candidates are kept and allowed to reproduce. Multiple copies are made of them, but the copies are not perfect; random changes are introduced during the copying process. These digital offspring then go on to the next generation, forming a new pool of candidate solutions, and are subjected to a second round of fitness evaluation. Those candidate solutions which were worsened, or made no better, by the changes to their code are again deleted; but again, purely by chance, the random variations introduced into the population may have improved some individuals, making them into better, more complete or more efficient solutions to the problem at hand. Again these winning individuals are selected and copied over into the next generation with random changes, and the process repeats. The expectation is that the average fitness of the population will increase each round, and so by repeating this process for hundreds or thousands of rounds, very good solutions to the problem can be discovered.
As astonishing and counterintuitive as it may seem to some, genetic algorithms have proven to be an enormously powerful and successful problem-solving strategy, dramatically demonstrating the power of evolutionary principles. Genetic algorithms have been used in a wide variety of fields to evolve solutions to problems as difficult as or more difficult than those faced by human designers. Moreover, the solutions they come up with are often more efficient, more elegant, or more complex than anything comparable a human engineer would produce. In some cases, genetic algorithms have come up with solutions that baffle the programmers who wrote the algorithms in the first place![4]
Examples of the sort of
sophisticated designs arrived at via the electronic equivalent of random
mutation and natural selection include:
- The design of a load-bearing truss that while using no more material than the standard load design was light, strong and far more able to damp out vibrations than other designs.[5]
- An antenna which while counterintuitive in design easily met design specifications.[6]
- The evolution of tactical battle plans for military battles that were far superior to those made by military experts.[7]
- Using genetic algorithms to classify targets based on radar reflections, with better accuracy than traditional methods.[8]
- Evolving the control systems required to control robot soccer players.[9]
- Scheduling aircraft landings at Heathrow Airport with more efficiency than human scheduling.[10]
Arguably one of the most
impressive examples of design produced via evolutionary algorithms was that of
the design of a voice recognition circuit using field programmable gate array
devices. Not only did the evolved design use far fewer logic gates than a human
engineer would use, the design was unlike anything an engineer would create:
The evolved system uses far fewer cells than
anything a human engineer could have designed, and it does not even need the
most critical component of human-built systems - a clock. How does it work?
Thompson has no idea, though he has traced the input signal through a complex
arrangement of feedback loops within the evolved circuit. In fact, out of the
37 logic gates the final product uses, five of them are not even connected to
the rest of the circuit in any way - yet if their power supply is removed, the
circuit stops working. It seems that evolution has exploited some subtle
electromagnetic effect of these cells to come up with its solution, yet the
exact workings of the complex and intricate evolved
structure remain a mystery.[11]
In short, we have an example of "irreducible complexity" produced by evolutionary algorithms. Creationist often like to claim that scientists and engineers who seek inspiration from nature are merely copying God, but the truth is that by employing evolutionary methods, we can achieve design far more elegant than what conscious, intelligent, human methods could achieve.
[1] MK-74 Guided Missile Fire Control System (GMFCS) http://www.fas.org/m...weaps/mk-74.htm
[2] Pettigrew J.D. “Electroreception in Monotremes” The
Journal of Experimental Biology (1999) 202:1447-1454
[3] Fong E “Electroreception in Platypus (Ornithorhynchus anatinus)” http://www.reed.edu/biology/professors/srenn/pages/teaching/web_2010/EmilyFong/phylogeny.html
[4] Marczyk A “Genetic Algorithms and Evolutionary
Complexity” TalkOrigins Archive April 23rd 2004 http://www.talkorigi...enalg.html#what Accessed 6th
January 2012
[5] Keane, A.J. and S.M. Brown. "The design of a
satellite boom with enhanced vibration performance using genetic algorithm
techniques." In Adaptive Computing in Engineering Design and Control
'96 - Proceedings of the Second International Conference, I.C. Parmee (ed),
p.107-113. University
of Plymouth, 1996
[6] Altshuler, Edward and Derek Linden. "Design of a
wire antenna using a genetic algorithm." Journal of Electronic Defense
(1997) 20(7):50-52
[7] Kewley, Robert and Mark Embrechts.
"Computational military tactical planning system." IEEE
Transactions on Systems, Man and Cybernetics, Part C - Applications and Reviews,
(2002) 32(2):161-171
[8] Hughes, Evan and Maurice Leyland. "Using
multiple genetic algorithms to generate radar point-scatterer models." IEEE
Transactions on Evolutionary Computation (2000) 4(2):147-163
[9] Andre, David and Astro Teller. "Evolving team
Darwin United." In RoboCup-98: Robot Soccer World Cup II, Minoru Asada and
Hiroaki Kitano (eds). Lecture Notes in Computer Science, vol.1604,
p.346-352. Springer-Verlag, 1999.
[10] Beasley, J.E., J. Sonander and P. Havelock.
"Scheduling aircraft landings at London Heathrow using a population
heuristic." Journal of the Operational Research Society
[11] Davidson,
Clive. "Creatures from primordial silicon." New Scientist
(1997) 156(2108):30-35
