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evolutionary-optimization-laboratory
rmoo:Multi-Objective Optimization in R
The 'rmoo' package is a framework for multi- and many-objective optimization, which allows researchers and users versatility in parameter configuration, as well as tools for analysis, replication and visualization of results. The 'rmoo' package was built as a fork of the 'GA' package by Luca Scrucca(2017) <DOI:10.32614/RJ-2017-008> and implementing the Non-Dominated Sorting Genetic Algorithms proposed by K. Deb's.
Maintained by Francisco Benitez. Last updated 5 months ago.
metaheuristicsmultiobjectivemultiobjective-optimizationnsgansga2nsga3optimizationpareto-front
16.6 match 30 stars 5.01 score 23 scripts
shramkoartem
nsga3:An Implementation of Non-Dominated Sorting Genetic Algorithm III for Feature Selection
An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm that solves multiple optimization problems simultaneously by applying a non-dominated sorting technique. It uses a reference points based selection operator to explore solution space and preserve diversity. See the original paper by K. Deb and H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.
Maintained by Artem Shramko. Last updated 6 years ago.
55.0 match 1.00 score 1 scripts
dots26
MaOEA:Many Objective Evolutionary Algorithm
A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' <doi:10.1016/j.ejor.2006.08.008> 'NSGA-III' <doi:10.1109/TEVC.2013.2281535> 'MO-CMA-ES' <doi:10.1145/1830483.1830573> The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and 'WFG4'-'WFG9' from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.
Maintained by Dani Irawan. Last updated 2 years ago.
3.3 match 6 stars 3.78 score 2 scripts