R-universe search: multiobjective

Showing 13 of total 13 results (show query)

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.

metaheuristics multiobjective multiobjective-optimization nsga nsga2 nsga3 optimization pareto-front

17.5 match 30 stars 5.01 score 23 scripts

fcampelo

MOEADr:Component-Wise MOEA/D Implementation

Modular implementation of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) [Zhang and Li (2007), <DOI:10.1109/TEVC.2007.892759>] for quick assembling and testing of new algorithmic components, as well as easy replication of published MOEA/D proposals. The full framework is documented in a paper published in the Journal of Statistical Software [<doi:10.18637/jss.v092.i06>].

Maintained by Felipe Campelo. Last updated 2 years ago.

moead multiobjective-optimization

8.0 match 20 stars 6.30 score 40 scripts

olafmersmann

emoa:Evolutionary Multiobjective Optimization Algorithms

Collection of building blocks for the design and analysis of evolutionary multiobjective optimization algorithms.

Maintained by Olaf Mersmann. Last updated 6 months ago.

7.2 match 8 stars 6.02 score 51 scripts 3 dependents

mlopez-ibanez

eaf:Plots of the Empirical Attainment Function

Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>.

Maintained by Manuel López-Ibáñez. Last updated 7 months ago.

eaf eaf-differences epsilon hypervolume inverted-generational-distance multiobjective-optimization summary-attainment-surfaces visualization gsl

7.5 match 18 stars 5.37 score 32 scripts 1 dependents

jlepird

prefeR:R Package for Pairwise Preference Elicitation

Allows users to derive multi-objective weights from pairwise comparisons, which research shows is more repeatable, transparent, and intuitive other techniques. These weights can be rank existing alternatives or to define a multi-objective utility function for optimization.

Maintained by John Lepird. Last updated 3 years ago.

bayesian-inference multiobjective-optimization preference-elicitation

7.5 match 1 stars 3.90 score 16 scripts

mbinois

GPareto:Gaussian Processes for Pareto Front Estimation and Optimization

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

Maintained by Mickael Binois. Last updated 1 years ago.

cpp

3.8 match 16 stars 5.96 score 38 scripts 1 dependents

bioc

ReactomeGraph4R:Interface for the Reactome Graph Database

Pathways, reactions, and biological entities in Reactome knowledge are systematically represented as an ordered network. Instances are represented as nodes and relationships between instances as edges; they are all stored in the Reactome Graph Database. This package serves as an interface to query the interconnected data from a local Neo4j database, with the aim of minimizing the usage of Neo4j Cypher queries.

Maintained by Chi-Lam Poon. Last updated 5 months ago.

dataimport pathways reactome network graphandnetwork

3.0 match 6 stars 5.26 score 6 scripts

cran

airGR:Suite of GR Hydrological Models for Precipitation-Runoff Modelling

Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A) that can be applied either on a lumped or semi-distributed way. A snow accumulation and melt model (CemaNeige) and the associated functions for the calibration and evaluation of models are also included. Use help(airGR) for package description and references.

Maintained by Olivier Delaigue. Last updated 1 years ago.

fortran

1.5 match 4 stars 6.60 score 164 scripts 4 dependents

jakobbossek

ecr:Evolutionary Computation in R

Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.

Maintained by Jakob Bossek. Last updated 1 years ago.

combinatorial-optimization evolutionary-algorithm evolutionary-algorithms evolutionary-strategy genetic-algorithm-framework metaheuristics multi-objective-optimization optimization optimization-framework cpp

1.3 match 43 stars 7.36 score 89 scripts 2 dependents

r-forge

stops:Structure Optimized Proximity Scaling

Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices.

Maintained by Thomas Rusch. Last updated 2 months ago.

openjdk

1.7 match 1 stars 4.48 score 23 scripts

cran

mogavs:Multiobjective Genetic Algorithm for Variable Selection in Regression

Functions for exploring the best subsets in regression with a genetic algorithm. The package is much faster than methods relying on complete enumeration, and is suitable for data sets with large number of variables. For more information, see Sinha, Malo & Kuosmanen (2015) <doi:10.1080/10618600.2014.899236>.

Maintained by Tommi Pajala. Last updated 7 years ago.

4.7 match 1.00 score

agvico

SDEFSR:Subgroup Discovery with Evolutionary Fuzzy Systems

Implementation of evolutionary fuzzy systems for the data mining task called "subgroup discovery". In particular, the algorithms presented in this package are: M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) <doi:10.1109/TFUZZ.2006.890662> M. J. del Jesus, P. Gonzalez, F. Herrera (2007) <doi:10.1109/MCDM.2007.369416> C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200> C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) <doi:10.1016/j.ins.2014.11.030> It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in KEEL, ARFF and CSV format and also with data.frame objects.

Maintained by Angel M. Garcia. Last updated 4 years ago.

1.7 match 2.53 score 34 scripts

alan9956

nsga2R:Elitist Non-Dominated Sorting Genetic Algorithm

Box-constrained multiobjective optimization using the elitist non-dominated sorting genetic algorithm - NSGA-II. Fast non-dominated sorting, crowding distance, tournament selection, simulated binary crossover, and polynomial mutation are called in the main program. The methods are described in Deb et al. (2002) <doi:10.1109/4235.996017>.

Maintained by Ming-Chang (Alan) Lee. Last updated 3 years ago.

0.5 match 3.12 score 29 scripts 5 dependents