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#' Chung Reynolds Function
#'
#' The defintion is given by
#' \deqn{f(\mathbf{x}) = \left(\sum_{i=1}^{n} \mathbf{x}_i^2\right)^2}
#' with box-constraings \eqn{\mathbf{x}_i \in [-100, 100], i = 1, \ldots, n}.
#'
#' @references C. J. Chung, R. G. Reynolds, CAEP: An Evolution-Based Tool for
#' Real-Valued Func- tion Optimization Using Cultural Algorithms, International
#' Journal on Artificial In- telligence Tool, vol. 7, no. 3, pp. 239-291, 1998.
#'
#' @template arg_dimensions
#' @template ret_smoof_single
#' @export
makeChungReynoldsFunction = function(dimensions) {
assertCount(dimensions)
force(dimensions)
makeSingleObjectiveFunction(
name = paste(dimensions, "-d Chung Reynolds Function", sep = ""),
id = paste0("chungReynolds", dimensions, "d"),
fn = function(x) {
assertNumeric(x, len = dimensions, any.missing = FALSE, all.missing = FALSE)
sum(x^2)^2
},
par.set = makeNumericParamSet(
len = dimensions,
id = "x",
lower = rep(-100, dimensions),
upper = rep(100, dimensions),
vector = TRUE
),
tags = attr(makeChungReynoldsFunction, "tags"),
global.opt.params = rep(0, dimensions),
global.opt.value = 0
)
}
class(makeChungReynoldsFunction) = c("function", "smoof_generator")
attr(makeChungReynoldsFunction, "name") = c("Chung Reynolds")
attr(makeChungReynoldsFunction, "type") = c("single-objective")
attr(makeChungReynoldsFunction, "tags") = c("single-objective", "unimodal", "continuous", "differentiable", "scalable")
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