Extremal optimization

Another piece in the puzzle is work on computational complexity, specifically that critical points have been shown to exist in NP-complete problems, where near-optimum solutions are widely dispersed and separated by barriers in the search space causing local search algorithms to get stuck or severely hampered.

This requires that a suitable representation be selected which permits individual solution components to be assigned a quality measure ("fitness").

This differs from holistic approaches such as ant colony optimization and evolutionary computation that assign equal-fitness to all components of a solution based upon their collective evaluation against an objective function.

A more detailed examination reveals some interesting principles, which may have applicability and even some similarity to broader population-based approaches (evolutionary computation and artificial immune system).

Although such punctuated-equilibrium behaviour can be "designed" or "hard-coded", it should be stressed that this is an emergent effect of the negative-component-selection principle fundamental to the algorithm.