Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2010.5586465
Title: Restricted Boltzmann machine based algorithm for multi-objective optimization
Authors: Tang, H.
Shim, V.A.
Tan, K.C. 
Chia, J.Y.
Issue Date: 2010
Source: Tang, H.,Shim, V.A.,Tan, K.C.,Chia, J.Y. (2010). Restricted Boltzmann machine based algorithm for multi-objective optimization. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 : -. ScholarBank@NUS Repository. https://doi.org/10.1109/CEC.2010.5586465
Abstract: Restricted Boltzmann machine is an energy-based stochastic neural network with unsupervised learning. This network consists of a layer of hidden unit and visible unit in an undirected generative network. In this paper, restricted Boltzmann machine is modeled as estimation of distribution algorithm in the context of multi-objective optimization. The probabilities of the joint configuration over the visible and hidden units in the network are trained until the distribution over the global state reach a certain degree of thermal equilibrium. Subsequently, the probabilistic model is constructed using the energy function of the network. Moreover, the proposed algorithm incorporates clustering in phenotype space and other canonical operators. The effects on the stability of the trained network and clustering in optimization are rigorously examined. Experimental investigations are conducted to analyze the performance of the algorithm in scalable problems with high numbers of objective functions and decision variables. © 2010 IEEE.
Source Title: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
URI: http://scholarbank.nus.edu.sg/handle/10635/71636
ISBN: 9781424469109
DOI: 10.1109/CEC.2010.5586465
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

20
checked on Jan 9, 2018

Page view(s)

13
checked on Jan 19, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.