Reducing computational complexity of estimating multivariate histogram-based probabilistic model

In continuous domain, how to efficiently learn the complex probabilistic graphical model is a bottleneck problem for estimation of distribution algorithms (EDAs). The predominant researches focus on Gaussian probabilistic model instead of histogram distribution model because of its comparative superiority in the computational complexity. In this paper, however, we find that using the histogram model does not necessarily bring into exponential computational complexity. Based on the fact many bins are zero-height, we propose a novel method that can learn the multivariate dependency histogram based probabilistic graphical model with acceptable polynomial computational complexity. Several strategies previously used in the HEDA are combined into the new algorithm to improve the convergence and diversity. Experiments showed the superior performance of the new algorithm on several continuous problems compared with UMDA
c
, IDEA-G and sur-shr-HEDA.