met_mlp: Train a Feed-Forward Multilayer Perceptron

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

Train a Feed-Forward Multilayer Perceptron

Description

Convenience wrapper around metann() for training feed-forward multilayer perceptrons.

Usage

met_mlp(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  architecture = NULL,
  hidden_layers = NULL,
  activation = "relu",
  output_activation = NULL,
  task = c("auto", "regression", "classification"),
  optimizer = optimizer_pso(),
  loss = NULL,
  metrics = NULL,
  seed = NULL,
  verbose = TRUE
)

Arguments

formula	Optional model formula.
data	Optional data frame used with formula.
x	Optional numeric input matrix or data frame.
y	Optional response vector.
architecture	Optional MLP architecture object.
hidden_layers	Integer vector giving the number of units in each hidden layer.
activation	Activation function for hidden layers.
output_activation	Optional output activation function. If NULL, it is selected automatically based on the task.
task	One of "auto", "regression", or "classification".
optimizer	Optimizer object.
loss	Optional loss function. If NULL, it is selected automatically based on the task.
metrics	Optional performance metrics. If NULL, default metrics are selected automatically based on the task.
seed	Optional random seed.
verbose	Logical. If TRUE, progress information is printed.

Value

An object of class "metann".

References

Montana, D. J., and Davis, L. (1989). Training Feedforward Neural Networks Using Genetic Algorithms. Proceedings of the 11th International Joint Conference on Artificial Intelligence, 762–767.

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

Karaboga, D., and Ozturk, C. (2009). Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification. Neural Network World, 19(3), 279–292.

Mirjalili, S. (2015). How Effective is the Grey Wolf Optimizer in Training Multi-Layer Perceptrons. Applied Intelligence, 43, 150–161. doi:10.1007/s10489-014-0645-7

Dilber, B., and Ozdemir, A. F. (2026). A novel approach to training feed-forward multi-layer perceptrons with recently proposed secretary bird optimization algorithm. Neural Computing and Applications, 38(5). doi:10.1007/s00521-026-11874-x

Examples

fit <- met_mlp(
  formula = Petal.Width ~ Sepal.Length + Sepal.Width + Petal.Length,
  data = iris,
  hidden_layers = c(5),
  optimizer = optimizer_pso(pop_size = 10, max_iter = 10),
  seed = 123,
  verbose = FALSE
)

fit

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