Classify optimization problem using selected ELA features and Random Forest model trained on BBOB dataset.

Classify optimization problem using selected ELA features and Random Forest model trained on BBOB dataset.

Usage

classify_optimization_problem(fitness, lower, upper)

Arguments

fitness

fitness function, that returns a numerical value, to be classified. The domain should be at least two dimensional.

lower

numeric - lower bound of the domain, a vector of length equal to the decision variables.

upper

numeric - upper bound of the domain, a vector of length equal to the decision variables.

Value

Returns one of c("low-conditioning", "multimodal-adequate", "multimodal-weak", "separable", "unimodal").

Examples

f <- function(x) x[[1]] + x[[2]]
result <- classify_optimization_problem(fitness = f, lower = c(-5, -5), upper = c(5, 5))