optimizer_de: Differential Evolution Optimizer

In metANN: Metaheuristic and Gradient-Based Optimization for Neural Network Training and Continuous Problems

Differential Evolution Optimizer

Description

Creates a Differential Evolution optimizer object.

Usage

optimizer_de(
  pop_size = 30,
  max_iter = 100,
  F = 0.5,
  CR = 0.9,
  strategy = "rand/1/bin"
)

Arguments

pop_size	Population size.
max_iter	Maximum number of iterations.
F	Differential weight. Common values are between 0.4 and 1.
CR	Crossover probability. Must be between 0 and 1.
strategy	Differential evolution strategy. Currently only "rand/1/bin" is supported.

Value

An object of class "met_optimizer".

References

Storn, R., and Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11, 341–359. doi:10.1023/A:1008202821328

Ilonen, J., Kamarainen, J.-K., and Lampinen, J. (2003). Differential Evolution Training Algorithm for Feed-Forward Neural Networks. Neural Processing Letters, 17, 93–105. doi:10.1023/A:1022995128597

Examples

optimizer_de()

metANN documentation built on May 16, 2026, 1:06 a.m.