Claim 6 - Random processes cannot create order
So many times I've seen arguments claiming that evolution of a living human
being is rather like a totally random process such as sending a tornado
through a junkyard and ending up with a Mercedes. This analogy is utterly
false. First I'll explain a little about randomness in science, and then I
thought I'd try a simple analogy with some of the numerical modelling work
that I've been doing recently.
Many people use the theory of thermodynamics, claiming that this
completely disproves evolution by showing that you cannot get order from
chaos. In fact, what the second law of thermodynamics says is this;
The total entropy of any closed system cannot ever decrease over time.
Entropy is a word used in chemistry and physics to describe many properties
of physical systems, and closely linked to the concept of disorder. Its
exact definition is related to number of different configurations of a
physical system. A good example of this is water. When water is frozen the
particles have very little space in which to move, therefore they have few
configurations. This means that the entropy of an ice cube is low. As the
ice melts, the solid water turns into liquid water, and the water molecules
now have far more positions to occupy so the entropy increases. Similarly
for evaporation - entropy increases again. So one might say that, if we
take an ice cube and thermally isolate it from the rest of the universe, then on
heating that ice cube from freezing through melting and boiling into water
vapour one has increased the entropy of the closed system as
predicted.
So what about freezing?
Well Creationists argue that the second law says that we cannot ever
decrease entropy. So therefore it should be totally impossible to freeze
water, right? Well clearly it isn't. Freezing water reduces the entropy of
the water, but yet this is not in disagreement with the second law of
thermodynamics. Why? Because the law states that we require a closed
system. That is, a system which is not in thermal contact with the rest
of the universe. In order to freeze a block of ice, one must somehow take
away the heat energy from the vapour so that it cools to liquid, and then
one must continue taking away heat energy until the liquid freezes. So
where did the entropy go? Simple - it has been moved by whatever mechanism
we used to cool down the water. It hasn't vanished - you can't destroy
energy - it has simply been moved elsewhere. This is how fridges and
freezers work - they shift the hot air from inside to the outside. If you
then examine the entire system, you see that the entropy has indeed
increased, as predicted, because the heat energy from the cooling water is
now redistributed elsewhere.
So how does this apply to evolution? Well we've just shown that it is very
possible to get order out of disorder - which goes exactly against what the
creationists argue, and totally agrees with evolution. For those who have
not yet decided to accept this point, consider the many other examples of
order out of chaos in the natural world. A good example is a star, like our
Sun. Now the Sun forms from an enormous, chaotic ball of gas, which
then collapses and reduces in size by many orders of magnitude until it is
the size of a star, when nuclear fusion begins. That, to me, is taking
chaos (the initial gas cloud), and creating order out of it (the star). How
did it happen? Well by gravity, of course. It's easy to create order out
of chaos so long as you have a driving force for that change. In evolution
we have natural selection as that driving force.
Still not convinced? Consider also snowflakes, crystals, precious gemstones
or even the growth of plants and animals. All of these seemingly create
order out of chaos, just like evolution, but in fact they are all very easy
to explain.
Evolutionary Computation - An Example
I'm going to describe the technique of evolutionary computation. Now this is
rather like biological evolution in many ways. It is a technique that is used
an awful lot in numerical calculations throughout all of science, maths,
engineering and probably much further afield than that too. I am also part of a
large research group at the University of Birmingham, which is one of the world
leaders in this technique, so I am clearly very familiary with the process. It
is partly because of my work on evolutionary computation that I am so convinced
by the power of natural evolution.
The problem is this : We have some challenge that we wish to solve. This might
be a mathematical function, where we want to find the maximum point, or it might
be a schedule, where we want to find the optimum use of time. In fact, it can
be pretty much anything provided you can define three simple properties:
- We must be able to represent a solution to the problem in a quantitative way
- We must have some notion of how 'good' a given solution is
- We must have some method for combining and/or altering existing solutions to create new potential solutions which preserve some or all of the original properties
In most of these problems, we're dealing with an absolutely vast solution space.
By that, I mean that it would be impossible to search through all of the
potential solutions in turn, looking for the optimum. For an example, I'll take
a simple, easy to visualise problem of finding the highest point in some terrain
map. If we consider a terrain that is, say, 100km on each side, then you can
see that a naive algorithm would have a lot of searching to do in order to find
the highest one metre square patch! In fact, the chances of finding that patch
at random are one in ten billion. But we can find it much more efficiently than
that!
Now imagine that you're a hiker lost in some mountains in dense fog. You want
to find the maximum point. If you just wander randomly around then what is the
chance of finding the highest point? Pretty low. So let's introduce an
evolutionary technique. Let's say you have a friend on the US military with
access to a height-mapping satellite. Let's propose an evolutionary algorithm
to solve this simple problem using this piece of technology and a bit of chance.
In each step, we're just using random numbers to do the decision-making, but
you'll quickly see how order evolves!
So let's just say we have a satellite which can measure terrain height in a very
small area, say 1 metre square. We choose a few speculative locations on our
terrain, say 100, and measure their heights above sea level. This is our first
'generation' of solutions. Then we decide which areas are most promising.
Those locations which turned out to be in low-lying swamps at 5 metres above sea
level might be discarded altogether, but those at 800 metres high are probably
along the right lines, especially if there are a lot of similarly high points
nearby.
We perform a second step by taking measurements at new positions. The new
positions are chosen randomly, but based on those locations which looked most
promising before. We randomly position our 100 'second-generation' points by
choosing promising locations and expanding near them. The higher the height of
a location, the higher the probability of placing new sample points nearby. For
example, if we found one point at 800m then we might want to place five or six
new points scattered randomly around this one original. However, the point at
200m might only have one new 'child' neighbour, and the point at 20m will have
none. Once we have placed 100 new points then we measure their heights, and
repeat the process. We have now replaced our original 100 random guesses with a
second population which we expect, on average, to do much better than the
first.
This is much like evolution. Random copying variations in the genotype of a
species cause some members of each population to be slightly more or less
fit to survive than their neighbours. If that fitness improves the chances
of that one organism then it will almost certainly survive. (We could add
in a bias probability here, assuming that some of these would still die, but
the overall result would clearly be the same.) However, if the mutation makes
them less fit then the chances are they would die out. The more unfit they
are the more likely they would be to die out.
So here I'm making the following analogy;
- Taking measurements randomly across the terrain = A population of individuals with different genotypes (and hence, phenotypes).
- The highest points have a larger influence on the next generation = The fittest individuals are most likely to survive long enough to pass on their genes.
- Low points are very likely to be ignored for the next generation = Individuals with low fitness are very unlikely to survive to pass on their genes.
- We're looking for the highest point on the landscape = we're looking to
evolve the fittest individual.
I have used this method a lot, and it is extremely efficient. The example
I gave was a 2D example (x,y) with a 2D function (height at location x,y). I've
dealt with hundreds of dimensions in my work, for example with model fitting.
Evolutionary computation has been applied to the
travelling salesman problem
where there can be thousands of nodes and an unimaginably large number of
possible solutions. It works extremely well, finding a very strong solution
extraordinarily quickly. It doesn't always find the best solution, but
then again, neither does nature!
At every step in the iteration, the code generates totally random
steps. These steps are then accepted or rejected using the probability that I
explained above. This is analogous to evolution where crossover, DNA mutations
and splicing cause random changes in the dominant characteristics of
individuals in a population. Those which improve the fitness are far more likely
to survive. Furthermore, parents which have high fitnesses are more likely to
survive for long enough to influence the subsequent generation.
I have seen my code work extremely well with an overwhelmingly complicated
dataset. For example, a travelling salesman problem (TSP) with 20 cities has a
potential solution set of 60 million billion potential tours. Working on
that one-by-one, at one million per second, would take around two thousand
years. Evolutionary computation can solve the problem in a few seconds with
ease, probably in half a dozen computational generations with a population of a
hundred individuals!
It's difficult to compare the two ideas, but it's clear to me that the process
is the same. However, natural evolution has had a few billion years to do the
job. That's a few billion iterations if the timescale between generations is
about a year. (Of course, early on it would probably have been enormously
shorter than this, perhaps hours or even minutes for a bacterium, for example.)
In addition, instead of making one random step in each generation, you're making
literally trillions, one for each creature of a species. If you consider the
early single-cell organisms then 'trillions' is certainly an
underestimate!
So perhaps you see now why these arguments that randomness cannot possibly
create order are completely false. Of course it can, if the randomness is
only a driver to a process which introduces a strong selection bias into the
results so that improvements are preferentially selected. So anyone who
says that the chance of assembling a complete human DNA by random process is
one in however many trillion is probably correct - but that's irrelevant -
it's not what's happening! That's not what evolution did. It didn't just
get three billion base pairs and randomly put them together - it iterated
the solution over billions (probably many trillions) of generations, with
trillions of organisms at every step each performing their own iterative
steps. Does it still seem all that implausible to you? Fascinating,
marvellous, even breathtaking, but certainly not implausible. In fact, I'd
go so far as to say that it is most definitely not only plausible, but
extremely likely.
One interesting result from this analogy is that it is totally possible to end
up with a smaller peak that is not the highest. Let's say that, just by luck,
our first generation managed to land one reading on the highest point in a
sub-optimal peak. We would then concentrate a large majority of our subsequent
readings around this one point, and possibly miss the global optimum that we had
not yet found.
Actually applying this theory to animals themselves is not necessarily valid -
the natural world is a co-evolving system, which means that the fitness of any
one organism is related to those around it. It isn't a fixed value. For
example, a lion is justifiably regarded as the king of the beasts when in the
middle of the Serengeti, but probably wouldn't fare too well if transported to
Antarctica, or the Pacific ocean. Similarly, a killer whale wouldn't survive
long in Tanzania and a Polar Bear has remarkable difficulty with perching in
trees. They are all adapted to life in a certain ecological niche, but you
could not assign a 'fitness' value to a living organism without specifying that
precise environment too. And that includes not just the climate and geography, but
also the other animals in the neighbourhood. For example, a cheetah would find
it remarkably difficult to survive if there were no small herbivores to chase
(and eat).
However, this theory of suboptimal peaks can be applied to individual biological
elements. Human eyes, for example, are not as efficient as they could be - the
optical receptors are plugged in backwards! However, to move from the current
form to a much better design would require stepping through a large number of
much worse intermediate stages where individual receptors were gradually rotated
through the intermediate stages. It is said that evolution is blind - it has no
final goal in sight - it just surges onwards. Those intermediate stages would
be less fit than their competitors, and would therefore die out despite the fact
that they were on the way to a complex improvement - a distant peak! To continue
the analogy, we would never 'cross the valley'. Besides, in this case the
evolutionary pressure to develop a significantly better vision system isn't
particularly strong. Assuming our ancestors evolved in deep jungles, we
wouldn't need very good distance vision because we would never be able to see
very far anyway! However, for a hawk, being able to spot a tiny vole at the
range of hundreds of metres is clearly a very considerable benefit.
Think about rolling 100 six-sided dice. Let's say you keep the sixes, and roll
the rest again. If you repeat this a few times, only re-rolling the dice that
are not already showing sixes, then you're sure to get a six on every die
eventually. The chance of rolling one hundred sixes in one go is so small to be
effectively zero. However, if you use a random process and apply a non-random
selection effect, i.e. removing the dice that have already rolled sixes, then
you can easily create a highly non-random result.
If you don't yet see how random processes can create highly non-random results
then please write to me for more explanation. The claim that evolution is
completely random is a misunderstanding at the very heart of most negative views
of evolution, and I know that it is the keystone in helping a large number of
people to accept this beautiful theory.
Also, for those of you who program in C, here's a very short piece of code that demonstrates how the processes behind natural evolution can solve extremely unlikely problems.
Is this a fair representation? If not then drop me an email. Address below.
This page maintained by Colin Frayn.
Email .
Last Update : 2nd December, 2005