  The current investigations on hyper-heuristics design have sprung up in two
different flavours: heuristics that choose heuristics and heuristics that
generate heuristics. In the latter, the goal is to develop a problem-domain
independent strategy to automatically generate a good performing heuristic for
the problem at hand. This can be done, for example, by automatically selecting
and combining different low-level heuristics into a problem specific and
effective strategy. Hyper-heuristics raise the level of generality on automated
problem solving by attempting to select and/or generate tailored heuristics for
the problem at hand. Some approaches like genetic programming have been
proposed for this. In this paper, we explore an elegant nature-inspired
alternative based on self-assembly construction processes, in which structures
emerge out of local interactions between autonomous components. This idea
arises from previous works in which computational models of self-assembly were
subject to evolutionary design in order to perform the automatic construction
of user-defined structures. Then, the aim of this paper is to present a novel
methodology for the automated design of heuristics by means of self-assembly.
