The feature selection has been widely used to reduce the data dimensionality. Data reduction improve the classification performance, the approximation function, and pattern recognition systems in terms of speed, accuracy and simplicity. A strategy to reduce the number of features in local search are the sequential search algorithms. In this work is presented a feature selection method based on Sequential Forward Selection (SFS) and Feed Forward Neural Network (FFNN) to estimate the prediction error as a selection criterion. Three well-known database have been used to test the SFS-FFNN with Artificial Metaplasticity on Perceptron Multilayer (AMMLP). The AMMLP is a new method applied for classification of patterns. The results obtained by SFS-FFNN with AMMLP in classification accuracy are superior than obtained by conventional BP algorithm and other recent feature selection algorithms applied to the same database. By these reasons the proposed method SFS-FFNN with AMMLP is an interesting alternative to reduce the data dimensionality and provide a high accuracy.

In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of robots. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets' qualities that are represented as scalar values. Decision-making mechanism is distributed and robots autonomously choose their assignments taking into account targets' qualities and distances. We tested the scalability of the proposed DBA algorithm in terms of number of robots and number of targets. For that, the experiments were performed in the simulator for various sets of parameters, including number of robots, number of targets, and targets' utilities. Control parameters inherent to DBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased.

Salamanca has been ranked as one of the most polluted cities in Mexico. The industry in the area led to a major economic development and rapid population growth in the second half of the twentieth century. The concerning registered pollutants are sulfur dioxide (SO2) and particles in the order of 10 micrometers or less (PM10). The prediction of concentrations of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. This work presents a forecasting model to predict average concentration of PM10 for the next 24 hours. The proposed model uses a combination of Multilayer Perceptron Neural Network and clustering algorithm. The source database used contains historical time series of meteorological variables and concentrations of PM10 collected in three different stations in Salamanca. The clustering algorithms have been implemented in order to find relationships between PM10 and meteorological variables. These relationships will help us to get additional information that will be used in the prediction model. The proposed model was compared, for accuracy and validation purposes, with a simple Multilayer Perceptron and a multiple Linear Regression. The performance estimation is determined using the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The obtained results show the importance of this set of meteorological variables in the prediction of pollutant concentrations