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EEEA: Explicit Exploration Strategy for Evolutionary Algorithms

Implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. Along with this exploration search strategy, a set of four different Estimation of Distribution Algorithms (EDAs) are also implemented for solving optimization problems in continuous domains. The implemented explicit exploration strategy in this package is described in Salinas-Gutiérrez and Muñoz Zavala (2023) <doi:10.1016/j.asoc.2023.110230>.

Version: 1.0.0
Imports: mvtnorm
Published: 2025-04-24
DOI: 10.32614/CRAN.package.EEEA
Author: Rogelio Salinas Gutiérrez ORCID iD [aut, cre, cph], Pedro Abraham Montoya Calzada ORCID iD [aut, cph], Angel Eduardo Muñoz Zavala ORCID iD [aut, cph], Alejandro Fausto Cortés Salinas [aut, cph], Ilse Daniela Saldivar Olvera ORCID iD [aut, cph]
Maintainer: Rogelio Salinas Gutiérrez <rsalinas at correo.uaa.mx>
License: GPL-3
NeedsCompilation: no
CRAN checks: EEEA results

Documentation:

Reference manual: EEEA.pdf

Downloads:

Package source: EEEA_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): EEEA_1.0.0.tgz, r-oldrel (arm64): EEEA_1.0.0.tgz, r-release (x86_64): EEEA_1.0.0.tgz, r-oldrel (x86_64): EEEA_1.0.0.tgz

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