Francis Roy-Desrosiers, based on CUDAANN r6 project developed by Emre Caglar.
This package provides a feedforward Artificial Neural Network (ANN) optimized by Genetic Algorithm (GA). Modifications in the Genetic algorithm from CUDAANN r6 have been made to improve the speed, the convergence and the memory management.

This package provides a feedforward Artificial Neural
Network (ANN) optimized by Genetic Algorithm (GA).
Modifications in the Genetic algorithm from CUDAANN r6 have
been made to improve the speed, the convergence and the memory
management.

Artificial Neural Networks for Anomaly Detection
Training of general classification and regression neural networks using
gradient descent. Special features include a function for training autoencoders
as well as an implementation of replicator neural networks, for details see
Hawkins et al. (2012) <doi:10.1007/3-540-46145-0_17>. Multiple activation and
cost functions (including Huber and pseudo-Huber) are included, as well as L1
and L2 regularization, momentum, early stopping and the possibility to specify
a learning rate schedule. The package contains a vectorized gradient descent
implementation which facilitates faster training through batch learning.

Training of general classification and regression neural networks using
gradient descent. Special features include a function for training replicator
neural networks and a function for training autoencoders. Multiple activation and
cost functions (including Huber and pseudo-Huber) are supported, as well as L1
and L2 regularization, momentum, early stopping and the possibility to specify
a learning rate schedule. The package contains a vectorized gradient descent
implementation which facilitates faster training through batch learning.

Training of neural networks for classification and regression tasks
using mini-batch gradient descent. Special features include a function for
training autoencoders, which can be used to detect anomalies, and some
related plotting functions. Multiple activation functions are supported,
including tanh, relu, step and ramp. For the use of the step and ramp
activation functions in detecting anomalies using autoencoders, see
Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore,
several loss functions are supported, including robust ones such as Huber
and pseudo-Huber loss, as well as L1 and L2 regularization. The possible
options for optimization algorithms are RMSprop, Adam and SGD with momentum.
The package contains a vectorized C++ implementation that facilitates
fast training through mini-batch learning.