classify_optimization_problem: Classify optimization problem using selected ELA features and...

In WojtAcht/hms: Multipopulation Evolutionary Strategy HMS

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

Description

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))

WojtAcht/hms documentation built on March 16, 2024, 7:51 p.m.