Despite the surrogate-based two-level algorithms that have been proposed for accelerating the optimization procedures, it may be still expensive for large problems. Therefore, this paper proposes the exploration of the approximation characteristics of the wavelet functions to define a coarse subspace for this kind of approach with relatively few float point operations. The wavelet transform is used to create the coarse model in a two-level genetic algorithm (GA), which is applied to a set of benchmark test problems. Although the coarse model is simpler and less accurate than the fine model, it behaves similarly to this last one and the original function. Moreover, the approach prevented the convergence to local minima whenever the GA presented such behavior and it is faster than the use of principal components analysis.Index Terms-Genetic algorithm (GA), multi-level optimization, surrogate models, wavelets.

This paper proposes a simple implementation of genetic algorithm with dynamic seed to solve deterministic job shop scheduling problems. The proposed methodology relies on a simple indirect binary representation of the chromosome and simple genetic operators (one-point crossover and bit-flip mutation), and it works by changing a seed that generates a solution from time to time, initially defined by the original sequencing of the problem addressed, and then adopting the best individual from the past runs of the GA as the seed for the next runs. The methodology was compared to three different approaches found in recent researches, and its results demonstrate that despite not finding the best results, the methodology, while being easy to be implemented, has its value and can be a starting point to more researches, combining it with other heuristics methods that rely in GA and other evolutionary algorithms as well.

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Purpose This paper aims to propose an approach based upon the principal component analysis (PCA) to define a contribution rate for each variable and then select the main variables as inputs to a neural network for energy load forecasting in the region southeastern Brazil. Design/methodology/approach The proposed approach defines a contribution rate of each variable as a weighted sum of the inner product between the variable and each principal component. So, the contribution rate is used for selecting the most important features of 27 variables and 6,815 electricity data for a multilayer perceptron network backpropagation prediction model. Several tests, starting from the most significant variable as input, and adding the next most significant variable and so on, are accomplished to predict energy load (GWh). The Kaiser–Meyer–Olkin and Bartlett sphericity tests were used to verify the overall consistency of the data for factor analysis. Findings Although energy load forecasting is an area for which databases with tens or hundreds of variables are available, the approach could select only six variables that contribute more than 85% for the model. While the contribution rates of the variables of the plants, plus energy exchange added, have only 14.14% of contribution, the variable the stored energy has a contribution rate of 26.31% being fundamental for the prediction accuracy. Originality/value Besides improving the forecasting accuracy and providing a faster predictor, the proposed PCA-based approach for calculating the contribution rate of input variables providing a better understanding of the underlying process that generated the data, which is fundamental to the Brazilian reality due to the accentuated climatic and economic variations.

The authors examined 47 subjects affected by acute phlebothrombosis of the lower limbs by means of light-reflection-rheography (LRR). The diagnosis was based on the results of clinical, Doppler, and duplex scanner evaluations. The results were compared with those obtained in 30 healthy subjects (control group). The LRR examinations were performed by two methods: the one consisting of a passive execution of postural movements of the limb, the other using the technique of plethysmographic venous occlusion. The pathological LRR curves were characteristic for each type of disease and for the site of thrombosis and were different from the normal ones. The results obtained show the usefulness of the method and it potential in studying the collateral circulation and the effects of different therapies.