Abstract
The diffusion of innovation theory aims to explain how new ideas and practices are disseminated among social system members. A significant number of the existing models is based on the use of parameters which determine the process of innovation adoption, and rely on simple mathematical functions centered in the observation and description of diffusion patterns. These models enable a more explicit diffusion process study, but their use involves the estimation of diffusion coefficients, usually obtained from historical data or chronological series. This raises some application problems in contexts where there is no data or the data is insufficient. This paper proposes the use of evolutionary computation as an alternative approach for the simulation of innovation diffusion within organizations. To overcome some of the problems inherent to existing models an evolutionary algorithm is proposed based on a probabilistic approach. The results of the simulations that were done to validate the algorithm revealed to be very promissing in this context. Simulation experiment results are presented that reveals a very promising approach of the proposed model. © 2013 Springer-Verlag Berlin Heidelberg.

Abstract
In complexity theory, scheduling problem is considered as a NP-complete combinatorial optimization problem. Since Multi-Agent Systems manage complex, dynamic and unpredictable environments, in this work they are used to model a scheduling system subject to perturbations. Meta-heuristics proved to be very useful in the resolution of NP-complete problems. However, these techniques require extensive parameter tuning, which is a very hard and time-consuming task to perform. Based on Multi-Agent Learning concepts, this article propose a Case-based Reasoning module in order to solve the parameter-tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance. © 2013 Springer-Verlag Berlin Heidelberg.

Abstract
A simulation teaching experiment to control a gantry crane system is proposed. The control is performed both in open-loop and closed-loop. The open-loop control is based on the Posicast feedforward technique and the closed-loop control uses a two-degrees of freedom configuration. Posicast control is used as a pre-filter outside the feedback loop to enhance the set-point tracking response and a PID controller is used in the feedback loop to deal with disturbance rejection. Students are required to use a gantry crane animation, to visualize its movement promoting a better perception and control techniques understanding. The experiment was performed by undergraduate feedback control students which provided learning results through a survey questionnaire.

Abstract
The gravitational search algorithm is proposed to design PID control structures. The controller design is performed considering the objectives of set-point tracking and disturbance rejection, minimizing the integral of the absolute error criterion. A two-degrees-of-freedom control configuration with a feed-forward prefilter inserted outside the PID feedback loop is used to improve system performance for both design criteria. The prefilter used is a Posicast three-step shaper designed simultaneously with a PID controller. Simulation results are presented which show the merit of the proposed technique.

Abstract
Most machining tasks require high accuracy and are carried out by dedicated machine-tools. On the other hand, traditional robots are flexible and easy to program, but they are rather inaccurate for certain tasks. Parallel kinematic robots could combine the accuracy and flexibility that are usually needed in machining operations. Achieving this goal requires proper design of the parallel robot. In this chapter, a multi-objective particle swarm optimization algorithm is used to optimize the structure of a parallel robot according to specific criteria. Afterwards, for a chosen optimal structure, the best location of the workpiece with respect to the robot, in a machining robotic cell, is analyzed based on the power consumed by the manipulator during the machining process.

Abstract
This paper describes an approach for assessing the level of stress of users of mobile devices with tactile screens by analysing their touch patterns. Two features are extracted from touches: duration and intensity. These features allow to analyse the intensity curve of each touch. We use decision trees (J48) and support vector machines (SMO) to train a stress detection classifier using additional data collected in previous experiments. This data includes the amount of movement, acceleration on the device, cognitive performance, among others. In previous work we have shown the co-relation between these parameters and stress. Both algorithms show around 80% of correctly classified instances. The decision tree can be used to classify, in real time, the touches of the users, serving as an input to the assessment of the stress level.