On the performance of the Bayesian optimization algorithm with combined scenarios of search algorithms and scoring metrics

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Nametala:2022:GPEM,

• author = "Ciniro A. L. Nametala and Wandry R. Faria and Benvindo R. {Pereira Junior}",
• title = "On the performance of the Bayesian optimization algorithm with combined scenarios of search algorithms and scoring metrics",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2022",
• volume = "23",
• number = "2",
• pages = "193--223",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Algorithm design and analysis, Bayesian Network models, Bayesian Optimization Algorithm, Metaheuristics, Probabilistic model",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/cM9lS",
• DOI = "doi:10.1007/s10710-022-09430-2",
• size = "31 pages",
• abstract = "The Bayesian Optimization Algorithm (BOA) is one of the most prominent Estimation of Distribution Algorithms. It can detect the correlation between multiple variables and extract knowledge on regular patterns in solutions. Bayesian Networks (BNs) are used in BOA to represent the probability distributions of the best individuals. The Bayesian Networks construction is challenging since there is a trade-off between acuity and computational cost to generate it. This trade-off is determined by combining a search algorithm (SA) and a scoring metric (SM). The search algorithm is responsible for generating a promising Bayesian Networks and the scoring metric assesses the quality of such networks. Some studies have already analyzed how this relationship affects the learning process of a Bayesian Networks. However, such investigation had not yet been performed to determine the bond linking the selection of search algorithm and scoring metric and the BOA output quality. Acting on this research gap, a detailed comparative analysis involving two constructive heuristics and four scoring metrics is presented. The classic version of BOA was applied to discrete and continuous optimization problems using binary and floating-point representations. The scenarios were compared through graphical analyses, statistical metrics, and difference detection tests. The results showed that the selection of search algorithm and scoring metric affects the quality of the BOA results since scoring metrics that penalise complex Bayesian Networks models perform better than metrics that do not consider the complexity of the networks. This study contributes to a discussion on this metaheuristics practical use, assisting users with implementation decisions.",
• notes = "Sao Carlos School of Engineering, Universidade de Sao Paulo (USP), Sao Paulo, Brazil",

}

Genetic Programming entries for Ciniro Aparecido Leite Nametala Wandry R Faria Benvindo R Pereira Junior

Citations