• Pazos Sierra, Alejandro
• Dorado de la Calle, Julián
• Santos del Riego, Antonino
• Rabuñal Dopico, Juan Ramón
• Pedreira Souto, Nieves

• Applications
• Artificial Neural Networks
• Genetic Algorithms

In this paper, we state an evolution of the recurrent ANN (RANN) to enforce the persistence of activations within the neurons to create activation contexts that generate correct outputs through time. In this new focus we want to file more information in the neuron`s connections. To do this, the connection`s representation goes from the unique values up to a function that generates the neuron`s output. The training process to this type of ANN has to calculate the gradient that identifies the function. To train this RANN we developed a GA based system that find the best gradient set to solve each problem.