Chaotic time series have been successfully predicted with the EPNet algorithm through the evolution of artificial neural networks. However, the input feature selection problem has either not been fully explored before or has not been compared against other algorithms in the literature. This paper presents four algorithms derived from the classical EPNet algorithm to evolve the input feature selection in three different chaotic series: Logistic, Lorenz and Mackey-Glass. Additionally, some flaws in the prediction field that may be considered in future works are discussed. A comparison against previous work demonstrates that in most cases the specialization of the EPNet algorithm allows better solutions with a smaller number of generations.

Emotional regulation is a mechanism to adjust our behavior to the current environment. We use this mechanism to achieve goals and objectives. This paper proposes a model for emotional regulation in virtual agents based on biological evidence of human brain function. The evidence shows different brain activation during emotional regulation. The model defines the techniques that are implemented, the data flow, and the data processing in each brain area during the emotional regulation. In the case study, a virtual agent shows behavior changes when it takes into account the emotional regulation mechanism and when it does not. The agent has emotional memories; they are from previous experiences and help to provide the desired behavior.