Searching Parsimony Models with Genetic Algorithms

Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) . To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.


News

Version 0.9.2 (2018-05)

  • Keeps and shows the individual with the best validation score in the whole GA process. It can be different than the best parsimonious solution at the last generation.
  • Solved problems with 'suggestions'. This parameter can be set with an initial population matrix in order to continue the GA process.
  • The 'object' and 'iter_ini' parameters permit to continue the GA process by using a previous 'ga_parsimony' object by selecting an initial population from '[email protected]' and with GA settings of the previous GA process.
  • Included a new parameter 'path_name_to_save_iter'. If it is not NULL, the 'ga_parsimony' object is saved in each iteration in a file with that name.

Version 0.9.1 (2017-08)

  • First release on CRAN.

Reference manual

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install.packages("GAparsimony")

0.9.2 by F.J. Martinez-de-Pison, a year ago


https://github.com/jpison/GAparsimony


Browse source code at https://github.com/cran/GAparsimony


Authors: F.J. Martinez-de-Pison [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports stats, graphics, grDevices, utils

Depends on methods, foreach, iterators

Suggests parallel, doParallel, doRNG, knitr, lhs, MASS, caret, mlbench, e1071, nnet, kernlab


See at CRAN