Abstract
The project addresses the urgent need to enhance student interest and performance in science, technology, engineering, and mathematics (STEM) courses, while fostering skills that are important prerequisites for IT careers. In the near term, the project is helping Portuguese schools and students meet state wide academic standards. Over the long term, the project will help inspire and prepare a new generation of IT professionals. This paper presents the robotics model of the Micromouse Project, how it works, how it is implemented, and its results. The Micromouse Project includes, in addition to the annual competition, the development of a kit, workshops for students and their teachers, and dissemination activities. The project was started in 2011 and all the preliminary phases are concluded. First results show that there is great interest and participation of all stakeholders. It was necessary to add to the project a graphical language (Blockly, Scratch, etc.) in order to facilitate and stimulate the participation of younger students.

Abstract
Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations. Copyright

Abstract
This work presents the use of a genetic algorithm to design a Mandani Fuzzy Controller with two inputs and one output, written in Matlab (R) environment, applied to a two axis positioning system using a robot. The robot has 6 degrees of freedom and is controlled with the objective of capturing an object on a workspace using a fuzzy controller. A genetic algorithm is used in order to determine the main characteristics of the membership functions of the fuzzy controller. The complete system employed to simulate the two axes positioning system uses the Transfer Function of two axes of the robot and the Fuzzy controller. In this work was implemented and simulated an operating scenario, being the results and the performance of the controller presented regarding the controller energy effort and the evolution of the (x,y) trajectories over time.

Abstract
The continuous changes in electricity markets' mechanisms and operations turn this environment into a challenging domain for the participating entities. Simulation tools are increasingly being used for decision support purposes of such entities. In particular, multi-agent based simulation, which facilitates the modeling of different types of mechanisms and players, is being fruitfully applied to the study of worldwide electricity markets. An effective decision support to market players' negotiations is, however, still not properly reached due to the uncertainty that results from the increasing penetration of renewable generation and the complexity of market mechanisms themselves. In this scope, this paper proposes a novel metalearner that provides decision support to market players in their negotiations. The proposed metalearner uses as input the output of several other market negotiation strategies, which are used to create a new, enhanced response. The final result is achieved through the combination and evolution of the strategies' learning results by applying a genetic algorithm.

Abstract
Wind energy production have been increasing in last years, with an annual growth of the installed capacity rate about 20%. It becomes important to develop optimization techniques to improve the effectiveness of the wind farms. One field in which this can be done is in the distribution network design that interconnects the turbines and the substation. This paper proposes two mathematical models to obtain the optimal electrical interconnection configuration of the wind farm turbines, considering technical constraints. One model minimizes the installation costs and the other one minimizes the installation costs and the energy losses costs registered during the wind farm lifetime. This problem corresponds to a capacitated minimum spanning tree with additional constraints. The proposed models were applied in two real wind farms. A sensitivity analysis is performed over two electrical parameters, the power factor and the load factor. The results show that the electrical losses of the wind farm must be taken into account in the optimization process.

Abstract
The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.