EVOLVING INDUCTIVE GENERALIZATION VIA GENETIC SELF-ASSEMBLY

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.