differential_evolution {niarules}

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
)

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.

Value

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