PBIL ensemble: many better than one

A `weak´ learning algorithm that performs just slightly better than random guessing can be `boosted´ into an arbitrarily accurate `strong´ learning algorithm by Schapire, R.E., (1990), Inspired from the `ensemble method´ idea, the paper proposes a novel conceptive model of EDA ensemble: a collection of EDAs are used to optimize the same problem, during the evolution process information interaction happens among EDAs, and at last optimum solutions can be obtained more likely than a single `strong´ EDA. As an instance, PBIL ensemble model is designed in details. Every PBIL serves as a component in PBIL ensemble and cooperate with others to efficiently accomplish an optimization process. Experiments on knapsack problems and function optimization problems show that PBIL ensemble exhibits better performance than simple GA and PBIL. And amazingly, to the GA-hard problem, e.g. 4-order fully deceptive problem, PBIL ensemble can achieve the optimal solution almost all the time