TY - GEN
T1 - A Bayesian based Hyper-Heuristic approach for global optimization
AU - Oliva, DIego
AU - Martins, Marcella S.R.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
KW - Bayesian networks
KW - estimation of distribution algorithms
KW - Global continuous optimization
KW - hyper-heuristics
UR - http://www.scopus.com/inward/record.url?scp=85071306257&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071306257&partnerID=8YFLogxK
U2 - 10.1109/CEC.2019.8790028
DO - 10.1109/CEC.2019.8790028
M3 - Conference contribution
AN - SCOPUS:85071306257
T3 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
SP - 1766
EP - 1773
BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019
Y2 - 10 June 2019 through 13 June 2019
ER -