differential_evolution: Implementation of Differential Evolution metaheuristic...

In niarules: Numerical Association Rule Mining using Population-Based Nature-Inspired Algorithms

Implementation of Differential Evolution metaheuristic algorithm.

Description

This function uses Differential Evolution, a stochastic population-based optimization algorithm, to find the optimal numerical association rule.

Usage

differential_evolution(
  d = 10,
  np = 10,
  f = 0.5,
  cr = 0.9,
  nfes = 1000,
  features,
  data,
  is_time_series = FALSE
)

Arguments

d	Dimension of the problem (default: 10).
np	Population size (default: 10).
f	The differential weight, controlling the amplification of the difference vector (default: 0.5).
cr	The crossover probability, determining the probability of a component being replaced (default: 0.9).
nfes	The maximum number of function evaluations (default: 1000).
features	A list containing information about features, including type and bounds.
data	A data frame representing instances in the dataset.
is_time_series	A boolean indicating whether the dataset is time series.

Value

A list containing the best solution, its fitness value, and the number of function evaluations and list of identified association rules.

References

Storn, R., & Price, K. (1997). "Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces." Journal of Global Optimization, 11(4), 341–359. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1023/A:1008202821328")}

niarules documentation built on Sept. 15, 2025, 5:08 p.m.