TY - JOUR A2 - Chen, Diyi AU - Bao, Lin AU - Sun, Xiaoyan AU - Chen, Yang AU - Man, Guangyi AU - Shao, Hui PY - 2018 DA - 2018/11/01 TI - Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems SP - 2609014 VL - 2018 AB - A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state-of-the-art surrogate-assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost. SN - 1076-2787 UR - https://doi.org/10.1155/2018/2609014 DO - 10.1155/2018/2609014 JF - Complexity PB - Hindawi KW - ER -