Towards memoryless model building
Dávid Iclănzan, Dumitru Dumitrescu · 2008
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical properties of multiple samples (population) scattered in the search space and generate offspring according to this model. The memory requirements of these methods grow along with the problem size as the population must be large enough to guarantee proper initial-supply, decision-making and accurate model-building.