dplbnDE

Implements Differential Evolution (DE) to train parameters of Bayesian Networks for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Any given Bayesian Network structure encodes assumptions about conditional independencies among the attributes and will result in an error if they do not hold in the data. Such an error includes the classification dimension. The main goal of Discriminative learning is to minimize this type of error. This package provides main variants of differential evolution described in Price & Storn (1996) doi:10.1109/ICEC.1996.542711 and recent ones, described in Tanabe & Fukunaga (2014) doi:10.1109/CEC.2014.6900380 and Zhang & Sanderson (2009) doi:10.1109/TEVC.2009.2014613 with adaptation mechanism for factor mutarion and crossover rate.