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#' Blend Alpha Beta recombination for DE
#'
#' Implements the "/blxAlphaBeta" (Blend Alpha Beta) recombination for the ExpDE
#' framework
#'
#' This routine also implements two special cases:
#' \itemize{
#' \item BLX-alpha recombination (\code{blxAlpha}), by setting
#' \code{recpars$alpha = recpars$beta});
#' \item Flat recombination (\code{flat}), by setting
#' \code{recpars$alpha = recpars$beta = 0})
#' }
#'
#' @section Recombination Parameters:
#' The \code{recpars} parameter contains all parameters required to define the
#' recombination. \code{recombination_blxAlpha()} understands the following
#' fields in \code{recpars}:
#' \itemize{
#' \item \code{alpha} : extrapolation parameter for 'best' parent vector.\cr
#' Accepts real value \code{0 <= alpha <= 0.5}.
#' \item \code{beta} : extrapolation parameter for 'worst' parent vector.\cr
#' Accepts real value \code{0 <= beta <= 0.5}.
#' }
#'
#' @section Warning:
#' This recombination operator evaluates the candidate solutions in \code{M},
#' which adds an extra \code{popsize} evaluations per iteration.
#'
#' @section References:
#' F. Herrera, M. Lozano, A. M. Sanchez, "A taxonomy for the crossover
#' operator for real-coded genetic algorithms: an experimental study",
#' International Journal of Intelligent Systems 18(3) 309-338, 2003.
#'
#' @param X population matrix (original)
#' @param M population matrix (mutated)
#' @param recpars recombination parameters (see \code{Recombination parameters}
#' for details)
#'
#' @return Matrix \code{U} containing the recombined population
#'
#' @export
recombination_blxAlphaBeta <- function(X, M, recpars) {
# Get access to variables in the calling environment
env <- parent.frame()
# ========== Error catching and default value definitions
assertthat::assert_that(is.matrix(X), is.numeric(X),
is.matrix(M), is.numeric(M),
assertthat::are_equal(dim(X), dim(M)),
all(assertthat::has_name(recpars,
c("alpha", "beta"))),
is.numeric(recpars$alpha), is.numeric(recpars$beta),
is_within(recpars$alpha, 0, 0.5),
is_within(recpars$beta, 0, 0.5),
all(assertthat::has_name(env,
c("J", "probpars", "nfe"))))
# ==========
# Performance values of the current population (X)
f.X <- env$J
#Evaluate population M
f.M <- evaluate_population(probpars = env$probpars,
Pop = M)
# Update NFE counter in calling environment
env$nfe <- env$nfe + nrow(M)
# Get best parent indicator matrix
X.is.best <- matrix(rep(f.X <= f.M,
times = ncol(X)),
ncol = ncol(X),
byrow = FALSE)
# Get infimum and supremum values, and interval lengths
Cmin <- pmin(X, M)
Cmax <- pmax(X, M)
I <- Cmax - Cmin
# Get 'best' and 'worst' parents
C1 <- X * X.is.best + M * !X.is.best
C2 <- M * X.is.best + X * !X.is.best
S <- (C1 <= C2)
# Return recombined population
return(pmin(C1, C2) -
I * (recpars$alpha * S + recpars$beta * !S) +
randM(X) * (I * ( 1 + recpars$alpha + recpars$beta)))
}
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