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UMDA: Univariate Marginal Distribution Algorithm

Implements the Univariate Marginal Distribution Algorithm (UMDA), an Estimation of Distribution Algorithm (EDA) for continuous optimization problems. The method iteratively selects the best individuals from a population, estimates an independent marginal probability distribution for each decision variable, and generates new candidate solutions by sampling from the estimated distributions. This process allows the probability model to adapt toward promising regions of the search space. The implementation supports normal, triangular, histogram-based, and uniform probability distributions, together with an optional explicit exploration strategy for the initialization of the population. The implemented explicit exploration strategy in this package is described in Salinas Gutierrez and Muñoz Zavala (2023) <doi:10.1016/j.asoc.2023.110230>.

Version: 0.1.0
Imports: EEEA
Published: 2026-10-10
DOI: 10.32614/CRAN.package.UMDA
Author: Rogelio Salinas Gutiérrez ORCID iD [aut, cre, cph], Juan Alberto Dávila del Alto ORCID iD [aut, cph], Jhon Daniel Aguilar Payares ORCID iD [aut, cph], Jonathan Michell Jauregui Carranza ORCID iD [aut, cph], Hiram Efraim Macias Ruelas ORCID iD [aut, cph], Byron Axel Morales Gutiérrez ORCID iD [aut, cph], María Fernanda Nieto Guerrero ORCID iD [aut, cph], Manuel Alonso Segoviano Baltazar ORCID iD [aut, cph], Pedro Abraham Montoya Calzada ORCID iD [aut, cph]
Maintainer: Rogelio Salinas Gutiérrez <rogelio.salinas at edu.uaa.mx>
License: GPL-3
NeedsCompilation: no
CRAN checks: UMDA results

Documentation:

Reference manual: UMDA.html , UMDA.pdf

Downloads:

Package source: UMDA_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: UMDA_0.1.0.zip
macOS binaries: r-release (arm64): UMDA_0.1.0.tgz, r-oldrel (arm64): UMDA_0.1.0.tgz, r-release (x86_64): UMDA_0.1.0.tgz, r-oldrel (x86_64): UMDA_0.1.0.tgz

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.