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#' Linear BGA recombination for DE
#'
#' Implements the "/lbga" (Linear Breeder Genetic Algorithm) recombination for
#' the ExpDE framework
#'
#' @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.\cr
#' D. Schlierkamp-voosen , H. Muhlenbein, "Strategy Adaptation by
#' Competing Subpopulations", Proc. Parallel Problem Solving from Nature
#' (PPSN III), 199-208, 1994.
#'
#' @param X population matrix (original)
#' @param M population matrix (mutated)
#' @param ... optional parameters (unused)
#'
#' @return Matrix \code{U} containing the recombined population
#'
#' @export
recombination_lbga <- function(X, M, ...) {
# ========== Error catching and default value definitions
# Get access to variables in the calling environment
env <- parent.frame()
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(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 'best' and 'worst' parents
C1 <- X * X.is.best + M * !X.is.best
C2 <- M * X.is.best + X * !X.is.best
# Set recombination parameters.
eps <- 1e-15
Lambda <- (C2 - C1) / matrix(rep(sqrt(rowSums((C1 - C2) ^ 2))+ eps, ncol(X)),
ncol = ncol(X),
byrow = FALSE)
mr <- matrix(stats::runif(nrow(X) * 16),
ncol = 16) <= 1 / 16
ms <- matrix(rep((2^-(0:15)),
times = nrow(X)),
ncol = 16,
byrow = TRUE)
delta <- matrix(rep(rowSums(mr * ms),
times = ncol(X)),
ncol = ncol(X),
byrow = FALSE)
# Return recombined population
return (C1 + 0.5 * sign(0.1 - randM(X)) * delta * Lambda)
}
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