On Updating Probabilistic Graphical Models in Bayesian Optimisation Algorithm

Mohamed El Yafrani, Marcella Scoczynski, Myriam Delgado, Ricardo Luders, Inkyung Sung, Markus Wagner, Diego Oliva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Bayesian Optimisation Algorithm (BOA) is an Estimation of Distribution Algorithm (EDA) that uses a Bayesian network as probabilistic graphical model (PGM). During the evolutionary process, determining the optimal Bayesian network structure by a given solution sample is an NP-hard problem resulting in a very time-consuming process. However, we show in this paper that significant changes in PGM structure do not occur so frequently, and can be particularly sparse at the end of evolution. A statistical study of BOA is thus presented to characterise a pattern of PGM adjustments that can be used as a guide to reduce the frequency of PGM updates. This is accomplished by proposing a new BOA-based optimisation approach (FBOA) whose PGM is not updated at each iteration. This new approach avoids the computational burden usually found in the standard BOA. Inspired by fitness landscape analysis concepts, we perform an investigation in the search space of an NK-landscape optimisation problem and compare the performances of both algorithms by using the correlation between the landscape ruggedness of the problem and the expected runtime of the algorithms. The experiments show that FBOA presents competitive results with significant saving of computational time.

Original languageEnglish
Title of host publicationProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages311-316
Number of pages6
ISBN (Electronic)9781728142531
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event8th Brazilian Conference on Intelligent Systems, BRACIS 2019 - Salvador, Bahia, Brazil
Duration: 15 Oct 201918 Oct 2019

Publication series

NameProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019

Conference

Conference8th Brazilian Conference on Intelligent Systems, BRACIS 2019
CountryBrazil
CitySalvador, Bahia
Period15.10.1918.10.19

Keywords

  • Bayesian Networks
  • Estimation of Distribution Algorithms
  • Model-based Metaheuristics
  • Probabilistic Graphical Models

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Computational Mathematics
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications

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  • Cite this

    El Yafrani, M., Scoczynski, M., Delgado, M., Luders, R., Sung, I., Wagner, M., & Oliva, D. (2019). On Updating Probabilistic Graphical Models in Bayesian Optimisation Algorithm. In Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019 (pp. 311-316). [8923860] (Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BRACIS.2019.00062