In the fitness landscape representation all attractors are not equal: those with a higher fitness are in a sense "better" than the others. For self-organizing systems, "better" or "fitter" usually means "more stable" or "with more potential for growth". However, the dynamics implied by a fitness landscape does not in general lead to the overall fittest state: the system has no choice but to follow the path of steepest descent. This path will in general end in a local minimum of the potential, not in the global minimum.
Apart from changing the fitness function, the only way to get the system out of a local minimum is to add a degree of indeterminism to the dynamics, that is, to give the system the possibility to make transitions to states other than the locally most fit one. This can be seen as the injection of "noise" or random perturbations into the system, which makes it deviate from its preferred trajectory. Physically, this is usually the effect of outside perturbations (e.g. vibrations, or shaking of the system) or of intrinsic indeterminacy (e.g. thermal or quantum fluctuations, or simply unknown factors that have not been incorporated into the state description). Such perturbations can "push" the system upwards, towards a higher potential. This may be sufficient to let the system escape from a local minimum, after which it will again start to descend towards a possibly deeper valley.
In general, the deeper the valley, the more difficult it will be for a perturbation to make a system leave that valley. Therefore, noise will in general make the system move out of the more shallow, and into the deeper valleys. Thus, noise will in general increase fitness. The stronger the noise the more the system will be able to escape the relatively shallow valleys, and thus reach a potentially deeper valley. However, a system with noise will never be able to really settle down in a local or global minimum, since whatever level of fitness it reaches it will still be perturbed and pushed into less fit states.
The most effective use of noise to maximize self-organization is to start with large amounts of noise which are then gradually decreased, until the noise disappears completely. The initially large perturbations will allow it to escape all local minima, while the gradual reduction will allow it to settle down in what is hopefully the deepest valley. This is the principle underlying annealing, the hardening of metals by gradually reducing the temperature, thus allowing the metal molecules to settle in the most stable crystalline configuration. The same technique applied to computer models of self-organization is called simulated annealing.
