Abstract
This article presents the new Estimation of Distribution Algorithms, EDABs, which apply the bootstrap to the process of estimation of distribution. EDABs choose the bootstrap samples from the population and construct the bootstrap distribution of the fitnesses. Then EDABs select the globally optimal individual from the distribution and estimate the distribution of the next population based on it. We evaluate the performance of EDABs using order-3 deceptive problem. We find population size significantly influences the performance, but bootstrap replication does not. The interesting result is that the ratio of the globally optimal individual in the bootstrap distribution and the population decreases as population size increases. We must find out why it happens and how the ratio of the globally optimal individual can be increased.
