  We propose that genetic encoding of self-assembling components greatly
enhances the evolution of complex systems and provides an efficient platform
for inductive generalization, i.e. the inductive derivation of a solution to a
problem with a potentially infinite number of instances from a limited set of
test examples. We exemplify this in simulations by evolving scalable circuitry
for several problems. One of them, digital multiplication, has been intensively
studied in recent years, where hitherto the evolutionary design of only
specific small multipliers was achieved. The fact that this and other problems
can be solved in full generality employing self-assembly sheds light on the
evolutionary role of self-assembly in biology and is of relevance for the
design of complex systems in nano- and bionanotechnology.
