TY - GEN
T1 - Bayesian inference in estimation of distribution algorithms
AU - Gallagher, Marcus
AU - Wood, Ian
AU - Keith, Jonathan
AU - Sofronov, George
N1 - Copyright 2007 IEEE. Reprinted from Evolutionary Computation, 2007, CEC 2007, IEEE Congress on : date 25-28 Sept. 2007. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
PY - 2007
Y1 - 2007
N2 - Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems. The model fitting task in this class of algorithms has largely been carried out to date based on maximum likelihood. An alternative approach that is prevalent in statistics and machine learning is to use Bayesian inference. In this paper, we provide a framework for the application of Bayesian inference techniques in probabilistic model-based optimization. Based on this framework, a simple continuous Bayesian Estimation of Distribution Algorithm is described. We evaluate and compare this algorithm experimentally with its maximum likelihood equivalent, UMDAc G.
AB - Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems. The model fitting task in this class of algorithms has largely been carried out to date based on maximum likelihood. An alternative approach that is prevalent in statistics and machine learning is to use Bayesian inference. In this paper, we provide a framework for the application of Bayesian inference techniques in probabilistic model-based optimization. Based on this framework, a simple continuous Bayesian Estimation of Distribution Algorithm is described. We evaluate and compare this algorithm experimentally with its maximum likelihood equivalent, UMDAc G.
UR - http://www.scopus.com/inward/record.url?scp=77952750332&partnerID=8YFLogxK
U2 - 10.1109/CEC.2007.4424463
DO - 10.1109/CEC.2007.4424463
M3 - Conference proceeding contribution
AN - SCOPUS:77952750332
SN - 1424413400
SN - 9781424413409
SP - 127
EP - 133
BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, N.J
T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
Y2 - 25 September 2007 through 28 September 2007
ER -