parsimony_rerank: Function for reranking by complexity in parsimonious model...

In jpison/GAparsimony: Searching Parsimony Models with Genetic Algorithms

Function for reranking by complexity in parsimonious model selection process

Description

Promotes models with similar fitness but lower complexity to top positions.

Usage

parsimony_rerank(object, verbose=FALSE, ...)

Arguments

object	An object of class "ga_parsimony" resulting from a call to function ga_parsimony with keep_history parameter set to TRUE.
verbose	If it is TRUE shows additional information.
...	Further arguments passed to or from other methods.

Details

This method corresponds with the second step of parsimonious model selection (PMS) procedure. PMS works in the following way: in each GA generation, best solutions are first sorted by their cost, J. Then, in a second step, individuals with less complexity are moved to the top positions when the absolute difference of their J is lower than a object@rerank_error threshold value. Therefore, the selection of less complex solutions among those with similar accuracy promotes the evolution of robust solutions with better generalization capabilities.

Value

Return a vector with the new position of the individuals.

Author(s)

Francisco Javier Martinez de Pison. [e-mail]. EDMANS Group. https://edmans.webs.com/

See Also

ga_parsimony

Examples

library(GAparsimony)
object <- new("ga_parsimony", 
              rerank_error=0.2,
              best_score = 2.0,
              popSize = 4, 
              fitnessval = c(2.0, 1.9, 1.1, 1.0), 
              complexity=c(2,1,2,1))

pop_ini <- data.frame(fitnessval=object@fitnessval, 
                      complexity=object@complexity)
print("INITIAL POPULATION:")
print(pop_ini)

print("POPULATION ORDERED BY COMPLEXITY")
print(paste0("WHEN abs(diff(fitnessval)) < ",
             object@rerank_error,":"))
pop_ini[parsimony_rerank(object),]

jpison/GAparsimony documentation built on April 18, 2023, 8:34 p.m.