R/crtrp.R

In drizztxx/gatbxr: Genetic Algorithm Toolbox Implemented by R

#' @title Create a real-valued initial population
#'
#' @description
#' This function creates a population of given size of random real-values. 
#'
#' @usage
#' crtrp(Nind,FieldDR)
#'
#' @param Nind a value containing the number of individuals in the new 
#' population.
#' @param FieldDR a matrix of 2 rows by number of variables describing the
#' boundaries of each variable. 
#' 
#' @details
#' The first step in a genetic algorithm is to create an initial population consisting of 
#' random individuals. \code{crtrp} produces a matrix, \code{Chrom}, containing uniformly
#' distributed random values in its elements.
#' 
#' \code{Chrom = crtrp(Nind, FieldDR)} creates a random real-valued matrix of
#' size \code{Nind * Nvar}, where \code{Nind} specifies the number of individuals in the
#' population and \code{Nvar} the number of variables of each individual. \code{Nvar} is derived
#' from \code{FieldDR} with \code{Nvar = NCOL(FieldDR)}.
#' 
#' \code{FieldDR} is a matrix of size 2 * \code{Nvar} and contains
#' the boundaries of each variable of an individual. The first row contains the lower
#' bounds, the second row the upper bounds.
#' 
#' \code{FieldDR} is used in other functions \code{\link{mutate}}
#' 
#' @return
#' a matrix containing the random valued individuals of the
#' new population of size \code{Nind} by number of variables.
#' 
#' @seealso
#' \code{\link{mutbga}}, \code{\link{recdis}}, \code{\link{recint}}, \code{\link{reclin}}
#' @export
#' @author 
#' The original matlab implementation of crtrp was written by Hartmut Pohlheim and 
#' tested by Alex Shenfield. 
#' The R implementation was written by David Zhao. 
#' @examples
#' ## To create a random populatin of 6 individuals with 4 variables each:
#' ## Define boundaries on the variables,
#' FieldDR = matrix(c(-100,-50,-30,-20,
#'                     100,50,30,20),2,4,byrow=TRUE)
#' ## Create initial population
#' Chrom = crtrp(6,FieldDR)                     

crtrp <- function(Nind,FieldDR){  
  ## Check parameter consistency
  if (length(Nind) != 1) stop("Nind has to be a scalar") 
  mF <- NROW(FieldDR); Nvar <- NCOL(FieldDR)
  if (mF != 2) stop("FieldDR must be a matrix with 2 rows")
  
  ## Compute Vector with Range of variables and Vector with Lower value
  Range <- as.vector(diff(FieldDR))
  Lower <- as.vector(FieldDR[1,])
  
  ## Create initial population
  ## Each row contains one individual, the values of each variable uniformly
  ## distributed between lower and upper bound (given by FieldDR)
  res.list <- lapply(1:Nind,function(x) runif(Nvar) * Range + Lower)
  return(do.call(rbind,res.list))
}