limits oneself to constructing systems out of fairly small numbers of components whose behavior and interactions are somehow simple.
But systems in nature need not in general operate under the constraint that their behavior should be predictable or understandable. And what this means is that in a sense they can use any number of components of any kind—with the result, as we have seen in this book, that the behavior they produce can often be highly complex.
However, if natural selection is to be successful at systematically molding the properties of a system then once again there are limitations on the kinds of components that the system can have. And indeed, it seems that what is needed are components that behave in simple and somewhat independent ways—much as in traditional engineering.
At some level it is not surprising that there should be an analogy between engineering and natural selection. For both cases can be viewed as trying to create systems that will achieve or optimize some goal.
Indeed, the main difference is just that in engineering explicit human effort is expended to find an appropriate form for the system, whereas in natural selection an iterative random search process is used instead. But the point is that the conditions under which these two approaches work turn out to be not so different.
In fact, there are even, I suspect, similarities in quite detailed issues such as the kinds of adjustments that can be made to individual components. In engineering it is common to work with components whose properties can somehow be varied smoothly, and which can therefore be analyzed using the methods of calculus and traditional continuous mathematics.
And as it turns out, much as we saw in Chapter 7, this same kind of smooth variation is also what tends to make iterative search methods such as natural selection be successful.
In biological systems based on discrete genetic programs, it is far from clear how smooth variation can emerge. Presumably in some cases it can be approximated by the presence of varying numbers of repeats in the underlying program. And more often it is probably the result of combinations of large numbers of elements that each produce fairly random behavior.