  We determined scaling laws for the numerical effort to find the optimal
configurations of a simple model potential energy surface (PES) with a perfect
funnel structure that reflects key characteristics of the protein interactions.
Generalized Monte-Carlo methods(MCM, STUN) avoid an enumerative search of the
PES and thus provide a natural resolution of the Levinthal paradox. We find
that the computational effort grows with approximately the eighth power of the
system size for MCM and STUN, while a genetic algorithm was found to scale
exponentially. The scaling behaviour of a derived lattice model is also
rationalized.
