Going for the big fishes
Dávid Iclănzan, Dan Andrei Dumitrescu · 2008
A major challenge in the field of metaheuristics is to find ways to increase the size of problems that can be addressed reliably. Scalability of probabilistic model building methods, capable to rendering difficult, large problems feasible by identifying dependencies, have been previously explored but investigations had mainly concerned problems where efficient solving is possible with the exploitation of low order dependencies. This is due to the initial-supply population sizing, where the number of samples is lower bounded by the exponential of the order of dependencies covered by the probabilistic model. With an exponentially growing population, the impact of the model building on the overall complexity, can easily exceed the bound for the number of evaluations.