Parallel BMDA with Probability Model Migration

Evolutionary algorithms, EDA algorithms, island-based models, migration, learning of probability models

The paper presents a new concept of parallel bivariate marginal distribution algorithm using the stepping stone based model of communication with the unidirectional ring topology. The traditional migration of individuals is compared with a newly proposed technique of probability model migration. The idea of the new xBMDA algorithms is to modify the learning of classic probability model (applied in the sequential BMDA). In the first strategy, the adaptive learning of the resident probability model is used. The evaluation of pair dependency, using Pearson's chi-square statistics is influenced by the relevant immigrant pair dependency according to the quality of resident and immigrant subpopulation. In the second proposed strategy, the evaluation metric is applied for the diploid mode of the aggregated resident and immigrant subpopulation. Experimental results show that the proposed adaptive BMDA outperforms the traditional concept of individual migration.