For the past twenty years I’ve been advocating soft, bottom-up, massively parallel computing techniques and waging a war on top-down, serial, control-freak thinking. Yet, just lately, I seem to have found myself becoming increasingly disdainful of genetic algorithms and other evolutionary software techniques. What’s happening – have I become a traitor to my cause? I hope not; evolution undoubtedly works. I’ve used it many times out of academic interest and more than once against genuinely difficult, practical problems. In fact, it’s partly because it works that I have such a problem with it. The thing is, I don’t like what evolutionary research is doing to the field of Artificial Life.

A Runaway Success

A similar thing happened to Artificial Intelligence. Back in the heady days of AI’s "Brave New World," all sorts of things seemed possible and all kinds of mechanism were being entertained and explored. But then a nasty positive-feedback loop took hold. AI had big ambitions and it was clear that those ambitions would take considerable effort and time to achieve. In the meantime, the field had to justify itself by demonstrating some results. Enter the Expert System. Few would argue that Expert Systems are the embodiment of AI’s original dream. Lists of explicit, hand-coded rules can, at best, only be described as "intelligent" when qualified by inverted commas, since they fail to show key intelligent phenomena like self-learning, conceptual reasoning or creativity. Nevertheless, they do work! Expert systems are not, in my view, on the path leading to true artificial intelligence, yet within their intended domain they were highly successful. Consequently, they attracted funding. Instantly, natural selection kicked in with some positive feedback – expert systems attracted funds, which encouraged their further development, making them ever more successful and attracting ever more funds. Academics who worked on such knowledge-based systems naturally emphasized those ideas in their teaching, thus increasing the proportion of knowledge engineers in the population. To a large extent then, AI has evolved into one ecological niche, when many others originally held equal promise. I have nothing against expert systems per se but the broader field of AI has clearly suffered as a result of this "lock-in" effect.

Now the same thing appears to be happening in Artificial Life. A mere decade ago, the world was A-Life’s metaphorical oyster. People were working on cellular automata, neural networks, self-catalyzing reactions and morphogenesis, plus, of course, simulated evolution. These are all very difficult topics, but the first one to show real theoretical and practical success was undoubtedly simulated evolution, albeit in the abstract forms known as genetic programming and genetic algorithms. As I’ve suggested above, things with immediate practical value attract more funding than those that only show potential for the indefinite future (except, for some reason, particle physics). Is this the top of a slippery slope? Is Artificial Life about to suffer lock-in, until "AL" is synonymous with "GA"? I fear so. Based on a straw poll of the work I’m currently aware of, plus a few web searches, I conclude that the bulk of the work currently being done under the Artificial Life banner is connected with evolution. In fact, this ecological specialization goes even finer and I believe that, out of this broad evolutionary subset, most work is based on the one, rather abstract and "unnatural" method of genetic algorithms.