Genetic optimization of asteroid families’ membership

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorLourenço, M. V.F.-
Autor(es): dc.creatorCarruba, V.-
Data de aceite: dc.date.accessioned2025-08-21T22:14:50Z-
Data de disponibilização: dc.date.available2025-08-21T22:14:50Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-09-08-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3389/fspas.2022.988729-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249170-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249170-
Descrição: dc.descriptionAsteroid families are groups of asteroids with a common origin, such as prior collisions or the parent body’s rotational fission. In proper [a, e, sin(i)] element domains, they are generally observed using the hierarchical clustering technique (HCMs), but the method may be ineffective in high-density regions, where it may be unable to separate near families. Previous works employed a different technique in which nine different machine learning classification algorithms were applied to the orbital distribution in proper elements of 21 known family constituents for the goal of new members’ identification. Each algorithm’s optimal hyper-parameters for every family were extensively investigated, which proved to be a time-consuming and repetitive procedure. Herein, we used a genetic algorithm-based tool to identify the most optimal machine learning algorithm for the same studied asteroid families as an alternative to the originally utilized parameter search mode. When compared to the same evaluative metrics utilized in the previous machine learning application study, the precision values of the new genetic machine learning algorithms have been consistently comparable, demonstrating that this alternative technique can be satisfactorily efficient and fast.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionSchool of Natural Sciences and Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionSchool of Natural Sciences and Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionCNPq: 2021/2698 304168/2021-1-
Idioma: dc.languageen-
Relação: dc.relationFrontiers in Astronomy and Space Sciences-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectestimation-
Palavras-chave: dc.subjectmethods-
Palavras-chave: dc.subjectmethods data analysis-
Palavras-chave: dc.subjectmethods statistical-
Palavras-chave: dc.subjectminor planets-
Palavras-chave: dc.subjectminor planets and asteroids: general-
Título: dc.titleGenetic optimization of asteroid families’ membership-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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