CRAN
MOEADr 1.1.0
Component-Wise MOEA/D Implementation
Released Oct 24, 2017 by Felipe Campelo
This package can be loaded by Renjin but 5 out 44 tests failed.
Dependencies
Modular implementation of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) [Zhang and Li (2007),
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml
file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies> <dependency> <groupId>org.renjin.cran</groupId> <artifactId>MOEADr</artifactId> <version>1.1.0-b11</version> </dependency> </dependencies> <repositories> <repository> <id>bedatadriven</id> <name>bedatadriven public repo</name> <url>https://nexus.bedatadriven.com/content/groups/public/</url> </repository> </repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:MOEADr')
Test Results
This package was last tested against Renjin 0.9.2687 on Aug 24, 2018.
- Decomposition_methods.MSDL_rejects_invalid_inputs
- Decomposition_methods.MSDL_returns_correct_size
- Decomposition_methods.MSDL_returns_correct_values
- Decomposition_methods.MSLD_returns_unitary_weight_vectors_E1
- Decomposition_methods.MSLD_returns_unitary_weight_vectors_E2
- Decomposition_methods.SLD_decomposition_returns_correct_sizes_E1
- Decomposition_methods.SLD_decomposition_returns_correct_sizes_E2
- Decomposition_methods.SLD_decomposition_returns_unitary_weight_vectors_E1
- Decomposition_methods.SLD_decomposition_returns_unitary_weight_vectors_E2
- Decomposition_methods.Uniform_return_matrix_sum_to_one_E1
- Decomposition_methods.Uniform_return_matrix_sum_to_one_E2
- Decomposition_methods.Uniform_returns_correct_dimensions_E1
- Decomposition_methods.Uniform_returns_correct_dimensions_E2
- Decomposition_methods.Uniform_returns_unitary_weight_vectors_E1
- Decomposition_methods.Uniform_returns_unitary_weight_vectors_E2
- Scalarization_methods.Scalarization_returns_correct_values_E1
- Scalarization_methods.Scalarization_returns_correct_values_E2
- Scalarization_methods.Scalarization_returns_correct_values_E3
- Scalarization_methods.Scalarization_returns_correct_values_E4
- create_population-examples
- decomposition_msld-examples
- decomposition_sld-examples
- decomposition_uniform-examples
- evaluate_population-examples
- find_nondominated_points-examples
- generate_weights-examples
- get_constraint_methods-examples
- get_decomposition_methods-examples
- get_localsearch_methods-examples
- get_scalarization_methods-examples
- get_stop_criteria-examples
- get_update_methods-examples
- get_variation_operators-examples
- moead-examples
- plot.moead-examples
- preset_moead-examples
- print.moead-examples
- scalarization_awt-examples
- scalarization_ipbi-examples
- scalarization_pbi-examples
- scalarization_ws-examples
- scalarization_wt-examples
- summary.moead-examples
- testthat