A Bayesian based Hyper-Heuristic approach for global optimization

DIego Oliva, Marcella S.R. Martins

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


Several metaheuristics have been developed for global optimization. Most of them are designed for solving a specific problem at hand, and their use on a new implementation is a challenging task. Hyper-heuristics are strategies that support these issues, combining a metaheuristic in a high-level for selecting or generating simple heuristics from a low-level. The aim is to find nearoptimal solutions based on the feedback received during the search. Estimation of Distribution Algorithms (EDAs) have been applied as hyper-heuristics, using a Probabilistic Graphical Model (PGM) to extract and represent interactions between its low-level heuristics to provide high-valued problem solutions. In this paper, we consider an EDA based on Bayesian networks as PGM on a hyper-heuristic context which encompasses a heuristic selection approach to find the best combinations of different known simple heuristics. We compare our proposed approach named Hyper-heuristic approach based on Bayesian Optimization Algorithm (HHBOA) using CEC'05 benchmark functions among 9 optimization algorithms. The experimental results show that HHBOA is competitive, outperforming the other approaches, especially in terms of convergence, on most of the functions considered in this paper.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728121536
Publication statusPublished - Jun 2019
Externally publishedYes
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings


Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
CountryNew Zealand


  • Bayesian networks
  • estimation of distribution algorithms
  • Global continuous optimization
  • hyper-heuristics

ASJC Scopus subject areas

  • Computational Mathematics
  • Modelling and Simulation

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