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Solar irradiation prediction is an important problem in geosciences with direct applications in renewable energy. Recently, a high number of machine learning techniques have been introduced to tackle this problem, mostly based on neural networks and support vector machines. Gaussian process regression (GPR) is an alternative nonparametric method that provided excellent results in other biogeophysical parameter estimation. In this letter, we evaluate GPR for the estimation of solar irradiation. Noting the nonstationary temporal behavior of the signal, we develop a particular time-based composite covariance to account for the relevant seasonal signal variations. We use a unique meteorological data set acquired at a radiometric station that includes both measurements and radiosondes, as well as numerical weather prediction models. We show that the so-called temporal GPR outperforms ten state-of-the-art statistical regression algorithms (even when including time information) in terms of accuracy and bias, and it is more robust to the number of predictions used. [ABSTRACT FROM PUBLISHER]

The internationalization of a company is widely understood as the corporative strategy for growing through external markets. It usually embodies a hard process, which affects diverse activities of the value chain and impacts on the organizational structure of the company. There is not a general model for a successful international company, so the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy and market characteristics of the company at hand. This paper presents a novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data. Specifically, we propose a hybrid algorithm composed by a grouping-based harmony search (HS) approach and an extreme learning machine (ELM) ensemble. The proposed hybrid scheme further incorporates a feature selection method, which is obtained by means of a given group in the HS encoding format, whereas the ELM ensemble renders the final accuracy metric of the model. Practical results for the proposed hybrid technique are obtained in a real application based on the exporting success of Spanish manufacturing companies, which are shown to be satisfactory in comparison with alternative state-of-the-art techniques. [ABSTRACT FROM PUBLISHER]

A call center is a system used by companies to provide a number of services to customers, which may vary from providing simple information to gathering and dealing with complaints or more complex transactions. The design of this kind of system is an important task, since the trend is that companies and institutions choose call centers as the primary option for customer relationship management. This paper presents an evolutionary algorithm based on Dandelion encoding to obtain near-optimal service trees which represent the structure of the desired call center. We introduce several modifications to the original Dandelion encoding in order to adapt it to the specific problem of service tree design. Two search space size reduction procedures improve the performance of the algorithm. Systematic experiments have been tackled in order to show the performance of our approach: first, we tackle different synthetic instances, where we discuss and analyze several aspects of the proposed evolutionary algorithm, and second, we tackle a real application, the design of the call center of an Italian telecommunications company. In all the experiments carried out we compare our approach with a lower bound for the problem based on information theory, and also with the results of a Huffman algorithm we have used for reference. [ABSTRACT FROM AUTHOR]

In this paper, a method to teach advanced features of evolutionary algorithms (EAs), using a famous game known as Japanese puzzles is presented. The authors show that Japanese puzzles are constrained combinatorial optimization problems, that can be solved using EAs with different encodings, and are challenging problems for EAs. Other features, such as special operators and local search heuristics and its hybridization with genetic algorithms, can also be taught using these puzzles. The authors report an experience using this method in a course taught at the Universidad de Alcalá, Madrid, Spain. [ABSTRACT FROM AUTHOR]

In this paper, two evolutionary programming (EP) algorithms (classical EP and fast EP) are applied to design prototype lowpass Finite Impulse Response filters for use in a modulated filter bank. The chosen filter design technique is based on frequency-sampling (where the Fourier transform magnitudes of the filter are the objective variables). Design is simplified by constraining most of these values, leaving only a small number of values in the filter transition band to be optimized. The EP algorithms were used to determine the optimum values for this subset of values. Since there is an additional monotomc constraint on the transition band values, a modification to the EP algorithms was developed called variable limits evolutionary programming. Results indicate that a) both EP algorithms were insensitive to initial conditions, and reliably found the minimum values of the chosen objective functions, and b) the designed prototype filters are suitable to obtain near-perfect reconstruction filter banks, offering quality parameters comparable or better than systems obtained using other techniques. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid Hopfield network-genetic algorithm (GA) approach to tackle the terminal assignment (TA) problem. TA involves determining minimum cost links to form a communications network, by connecting a given set of terminals to a given collection of concentrators. Some previous approaches provide very good results if the cost associated with assigning a single terminal to a given concentrator is known. However, there are situations in which the cost of a single assignment is not known in advance, and only the cost associated with feasible solutions can be calculated. In these situations, previous algorithms for TA based on greedy heuristics are no longer valid, or fail to get feasible solutions. Our approach involves a Hopfield neural network (HNN) which manages the problem's constraints, whereas a GA searches for high quality solutions with the minimum possible cost. We show that our algorithm is able to achieve feasible solutions to the TA in instances where the cost of a single assignment in not known in advance, improving the results obtained by previous approaches. We also show the applicability of our approach to other problems related to the TA. [ABSTRACT FROM AUTHOR]

This article focuses on a twofold approach to improve the performance of genetic algorithms (GAs) in the feature selection problem (FSP). The feature selection problem (FSP) is an open issue in machine learning, which basically consists of finding a subset of input features that describes the underlying system structure as well or better than all available features. There is a more general problem, known as feature extraction problem, in which one has to construct a few features that encode all of the information contained in the available original ones; the FSP is, in fact a particularization of this problem.

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles. [ABSTRACT FROM PUBLISHER]

We present an evolutionary approach to speaker segmentation, an activity that is especially important prior to speaker recognition and audio content analysis tasks. Our approach consists of a genetic algorithm (GA), which encodes possible segmentations of an audio record, and a measure of mutual information between the audio data and possible segmentations, which is used as fitness function for the GA. We introduce a compact encoding of the problem into the GA which reduces the length of the GA individuals and improves the GA convergence properties. Our algorithm has been tested on the segmentation of real audio data, and its performance has been compared with several existing algorithms for speaker segmentation, obtaining very good results in all test problems. [ABSTRACT FROM AUTHOR]