The peak shopping period is upon us and, for most online shops, the effectiveness of their Google AdWords campaigns can make the difference between a fruitful festive season and a bleak new year.

It’s at just such a critical moment that Google is rolling out changes to its AdWords algorithms having successfully tested them on its users in Spain, Portugal and Latin America. The algorithms are used to determine where in the sponsored rankings your ad will appear so a change can mean a dip in your position and consequent loss of traffic which, as business hots up, can cost a lot of money. The knee-jerk reaction is then to increase the bid price which, of course, reduces profitability – again costing money.

To be successful in online marketing, you have to look beyond this immediate response and understand what Google is trying to achieve here. Google has always ranked ads according to two criteria: the “Quality Score” of what you’re offering and the price you’re prepared to pay. The Quality Score is, itself, made up of three factors – the historical performance of the ad (primarily, what percentage of searchers clicked it); the relevance of the ad text to the search term; and, finally, the quality of the landing page.

The changes Google is introducing have the effect of increasing the importance of the landing page in the Quality Score. Google’s explanation is that too many searchers are being sent to pages that aren’t entirely relevant to their search. They get frustrated and leave the site, which is bad news for both the advertiser and Google.

Your Adwords Control Panel will tell you what your Quality Score is and specifically how your landing page rates. If you’re finding that your ad is appearing lower than it previously was, now is the time to check whether your Quality Score has dropped and, if it does, go through Google’s landing page guidelines.

Scientists and mathematicians are more likely than the average to be atheists, or at least to choose evolution over creationism to explain what they see around them. This is for a very good reason. Excluding evolutionary biologists, for obvious reasons, scientists are used to seeing complex behaviour emerge from simple systems.

Take Conway’s Game of Life as a perfect example. With just four simple rules this cellular automaton throws up all sorts of complex, emergent behaviour, including several repeating patterns. If you regularly deal with such impressive output from simple rules, then it’s not much of a stretch to imagine monkeys turning into humans.

Simple rules minutely affect each and every iteration of a system, which over time creates huge, directed change. It’s a hard concept to grasp, but once you do, it’s easy to see its impact everywhere you look.

Computer scientists are more likely than most to come across this sort of behaviour, thanks to the field of artificial intelligence. In particular, those in one specific area of AI: the study of genetic algorithms. These algorithms solve incredibly difficult problems by mimicking natural evolution.

The way they work is incredibly simple; define a problem and the characteristics of an ideal solution, then set it running. That’s it.

The algorithm will take a set of randomly created solutions, compare them to the optimum, kill the poor performers and breed the better ones with each other. Throw in a little random mutation, and you’re all set. Within a few generations some staggering designs can emerge, sometimes so strange that no human engineer could ever have dreamed them up.

It sounds too good to be true, but it is. Demonstrably, reproducibly so. If at this point you’re sceptical, good, that’s a perfectly scientific attitude. Take a look at chapter 10 of this document, which runs through an extremely simple example.

It doesn’t just work for word games, though; so far it’s been used to design fusion reactors, create better load-balancing strategies in communications networks and NASA has even used it to develop more efficient antennae than was ever thought possible for a satellite. These things work, and are in use now, all around you.

Still, though, some people don’t believe it works. That’s understandable – if you’re an ardent believer in creationism, and a sceptic of evolution, then the field of genetic algorithms presents some interesting problems. You can throw the occasional spanner in the works of natural evolution, and the vast timescales involved make it easy to cast doubt on the theory for some, but seeing a computer evolve a perfect design in a matter of minutes is far harder to dismiss – even though it’s working in exactly the same way.

This brings me to the blog post that irritated me so much, written by Casey Luskin at Evolution News and Views. It criticises another article which details NASA’s fantastic work on the previously mentioned antennae.

“The presumption of evolutionary biologists, of course, is that these “brilliant designs” evolved by natural selection preserving random, but beneficial mutations. Engineers operating under such presumptions have thus tried to mimic not only the “brilliant designs,” but also the evolutionary processes that allegedly produced the designs,” says Luskin. “Did they use truly Darwinian “evolutionary computing?” The article goes on to discuss how design parameters were smuggled into the simulation, such that it really wasn’t a truly unguided Darwinian evolutionary scenario.”

Nothing was smuggled. The only things that the algorithm requires are details of the set of current solutions – analogous to a population of animals – and details of what an optimum solution will be like; low power use, highest efficiency, etc – which is analogous to the environment, weeding out poor solutions.

I had always hoped that genetic algorithms would help to convince evolution-sceptics to take a more thorough look at the evidence, but it seems that it’s just being added to the list of “incorrect” scientific theories. The problem is that the evidence is right there, routing your emails and phone calls, and whizzing above your head in orbit.