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:1.8081
- @Article{Nametala:2022:GPEM,
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author = "Ciniro A. L. Nametala and Wandry R. Faria and
Benvindo R. {Pereira Junior}",
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title = "On the performance of the Bayesian optimization
algorithm with combined scenarios of search algorithms
and scoring metrics",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2022",
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volume = "23",
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number = "2",
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pages = "193--223",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Algorithm
design and analysis, Bayesian Network models, Bayesian
Optimization Algorithm, Metaheuristics, Probabilistic
model",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/cM9lS",
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DOI = "doi:10.1007/s10710-022-09430-2",
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size = "31 pages",
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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.",
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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