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This work presents the detection results involving two common electrical disturbances: sags and swells. Variance, skewness and kurtosis have been used to improve statistical characterization. The measurement procedure is funded in the tuning of the signal under test via a sliding window over which computation is developed. Locking is possible because these power quality disturbances keep the frequency of the power line. Statistical features reveal the inherent properties of the signals: amplitude, frequency and symmetry. The paper primarily examines a number of synthetics in order to extract the theoretical statistical features. Then the algorithm is corroborated using real-life signals, obtaining an accuracy of 83%. This stage is part of the design of an instrument for the measurement of the power quality.

In this paper, we confront a variant of the cutting-stock problem with multiple objectives. The starting point is a solution calculated by a heuristic algorithm, termed SHRP, that aims to optimize the two main objectives, i.e. the number of cuts and the number of different patterns. Here, we propose a multi-objective genetic algorithm to optimize other secondary objectives such as changeovers, completion times of orders pondered by priorities and open stacks. We report experimental results showing that the multi-objective genetic algorithm is able to improve the solutions obtained by SHRPon the secondary objectives.

The Job Shop Scheduling is a paradigm of Constraint Satisfaction Problems that has interested to researchers over the last years. In this work we propose a Genetic Algorithm hybridized with a local search method that searches over the space of semi-active schedules and a heuristic seeding method that generates active schedules stochastically. We report results from an experimental study over a small set of selected problem instances of common use, and also over a set of big problem instances that clarify the influence of each method in the Genetic Algorithm performance.

Codification is a very important issue when a Genetic Algorithm is designed to dealing with a combinatorial problem. In this paper we introduce new codification schemas for the Job Shop Scheduling problem which are extensions of two schemas of common use, and are worked out from the concept of underlying probabilistic model. Someway the underlying probabilistic model of a codification schema accounts for a tendency of the schema to represent solutions in some region of the search space. We report results from an experimental study showing that in many cases any of the new schemas results to be much more efficient than conventional ones due to the new schema tends to represent more promising solutions than the others. Unfortunately the selection in advance of the best schema for a given problem instance is not an easy problem and remains still open.

MARS's s-boxes were generated using a new algorithm developed by the IBM team, which was supposedly able of producing secure s-boxes against both differential and linear cryptanalysis. In this paper we show this is not the case, because their strength against linear cryptanalysis is not better (in fact, it seems to be worse) that what could be expected if generated randomly.