An investigation on sampling technique for multi-objective restricted Boltzmann machine

Estimation of distribution algorithms are increasingly gaining research interest due to their linkage information exploration feature. Two main mechanisms which contribute towards the success of the algorithms are probabilistic modeling and sampling method. Recent attention has been directed towards the development of probabilistic building technique. However, research on the sampling approach is less developed. Thus, this paper carries out an investigation on sampling technique for a novel multi-objective estimation of distribution algorithm - multi-objective restricted Boltzmann machine. Two variants of a new sampling technique based on energy value of the solutions in the trained network are proposed to improve the efficiency of the algorithm. Probabilistic information which is usually clamped into marginal probability distribution may hinder the algorithm in producing solutions that have high linkage dependency between variables. The proposed approach will overcome this limitation of probabilistic modeling in restricted Boltzmann machine. The empirical investigation shows that the proposed algorithm gives promising result in term of convergence and convergence rate.