{rfName}
ED

Indexed in

APC

470,00 Euros
Elsevier
Transformative agreement with library

License and Use

Icono OpenAccess

Citations

3

Analysis of institutional authors

Soloviev, Vicente PCorresponding AuthorLarranaga, PedroAuthorBielza, ConchaAuthor

Share

December 3, 2024
Publications
>
Article
Sí

EDAspy: An extensible python package for estimation of distribution algorithms

Publicated to: NEUROCOMPUTING. 598 128043- - 2024-09-14 598(), DOI: 10.1016/j.neucom.2024.128043

Authors:

Soloviev, VP; Larrañaga, P; Bielza, C
[+]

Affiliations

Univ Politecn Madrid, Artificial Intelligence Dept, Campus Montegancedo, Madrid, Spain - Author

Abstract

Estimation of distribution algorithms (EDAs) are a type of evolutionary algorithms where a probabilistic model is learned and sampled in each iteration. EDAspy provides different state-of-the-art implementations of EDAs including the recent semiparametric EDA. The implementations are modularly built, allowing for easy extension and the selection of different alternatives, as well as interoperability with new components. EDAspy is totally free and open-source under the MIT license.
[+]

Keywords

AlgorithmArticleBarium compoundsBayesia n networksBayesian networkBayesian networksBenchmarkinBenchmarkingDistribution algorithmsDrug therapyEstimation of distribution algorithmEstimation of distributionsEvolutionary algorithmEvolutionary algorithmsHigh level languagesIterative methodsNew componentsOpen-sourceProbabilistic modelsProbability distributionsSemi parametric estimationState of the art

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal NEUROCOMPUTING due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 37/204, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-27:

  • Google Scholar: 3
  • Scopus: 1
[+]

Impact and social visibility

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/88284/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 168
  • Downloads: 63
[+]

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PÉREZ SOLOVIEV, VICENTE) and Last Author (BIELZA LOZOYA, MARIA CONCEPCION).

the author responsible for correspondence tasks has been PÉREZ SOLOVIEV, VICENTE.

[+]

Awards linked to the item

This work has been partially supported by the Spanish Ministry of Science and Innovation through the PID2022-139977NB-I00 and TED2021-131310B-I00 projects, and by the Autonomous Community of Madrid within the ELLIS Unit Madrid framework. Vicente P. Soloviev has been supported by the predoctoral grant FPI PRE2020-094828 from the Spanish Ministry of Science and Innovation.
[+]