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FuzzyPred is an unsupervised-learning data-mining algorithm, which allows extracting fuzzy predicates in normal forms from the data. This method is used to solve a descriptive task where it is not known the kind of relationships that are to be found. The goal is to find patterns that describe the data and their relationships. Due to the large set of solutions or search space that may have, it was modeled as an optimization problem, where metaheuristics are applied to find good solutions. FuzzyPred provides as a result a set of predicates, evaluated in each of the quality measures, in spite of the fact that only one of the measures is optimized (truth value). This paper introduces a multiobjective approach for FuzzyPred by using two of the most known multi-objective optimization techniques: Pareto technique (or pure multi-objective) and weighted factors. An experimental study is presented in order to compare the efficacy of both techniques in this problem. The results in several international databases show that better results are obtained by the pure multi-objective technique. [ABSTRACT FROM AUTHOR]

The energy efficiency of an electric system depends in great measure of the reactive power compensation that it is carried out. One of the roads more employees are the use of capacitors banks. A good selection of these, presupposes three aspects: the selection of the banks capacity, the compensator type to use, as well as the location in the low voltage system. References of methods that consider the three aspects in a same problem are based on the mathematical programming, but these cannot represent all the effects of the compensation and these based in technical of continuous programming, being this a complex optimization problem of discreet search space. In the paper, we have developed a method with the employment of the genetic algorithms, which facilitates a more exact problem formulation. The method has been programmed in MATLAB, version 7.5 (R2007b), using programming structures of Genetic Algorithm and Direct Search Toolbox.The effectiveness is shown in an example of real application to the low voltage system of a hotel installation of the Cubanacan Company in Camagüey. [ABSTRACT FROM AUTHOR]

In this paper, the design of a genetic algorithm that modifies and recalculates the values those describe the membership functions for a fuzzy controller, has showed. The controller is applied in a bio-film fermentative process to obtain an improvement in the dichromate's production (DCM). The membership functions act over the concentration of substrate in the aeration tank so that produces the homogenous double saturation kinetic of the DCM, and this is increased, obtaining an improvement in their production. This is shown by development of the numerical algorithm. [ABSTRACT FROM AUTHOR]