Emilio G. Ortíz-García

Short term wind speed prediction based on evolutionary support vector regression algorithms

Hyper-parameters estimation in regression Support Vector Machines (SVMr) is one of the main problems in the application of this type of algorithms to learning problems. This is a hot topic in which very recent approaches have shown very good results in different applications in fields such as bio-medicine, manufacturing, control, etc. Different evolutionary approaches have been tested to be hybridized with SVMr, though the most used are evolutionary approaches for continuous problems, such as evolutionary strategies or particle swarm optimization algorithms. In this paper we discuss the application of two different evolutionary computation techniques to tackle the hyper-parameters estimation problem in SVMrs. Specifically we test an Evolutionary Programming algorithm (EP) and a Particle Swarm Optimization approach (PSO). We focus the paper on the discussion of the application of the complete evolutionary-SVMr algorithm to a real problem of wind speed prediction in wind turbines of a Spanish wind farm.

Letters: Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks

Wind speed prediction is a very important part of wind parks management. Currently, hybrid physical-statistical wind speed forecasting models are used to this end, some of them using neural networks as the final step to obtain accurate wind speed predictions. In this paper we propose a method to improve the performance of one of these hybrid systems, by exploiting diversity in the input data of the neural network part of the system. The diversity in the data is produced by the physical models of the system, applied with different parameterizations. Two structures of neural network banks are used to exploit the input data diversity. We will show that our method is able to improve the performance of the system, obtaining accurate wind speed predictions better than the one obtained by the system using single neural networks.

Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions

The selection of hyper-parameters in support vector machines (SVM) is a key point in the training process of these models when applied to regression problems. Unfortunately, an exact method to obtain the optimal set of SVM hyper-parameters is unknown, and search algorithms are usually applied to obtain the best possible set of hyper-parameters. In general these search algorithms are implemented as grid searches, which are time consuming, so the computational cost of the SVM training process increases considerably. This paper presents a novel study of the effect of including reductions in the range of SVM hyper-parameters, in order to reduce the SVM training time, but with the minimum possible impact in its performance. The paper presents reduction in parameter C, by considering its relation with the rest of SVM hyper-parameters (@c and @e), through an approximation of the SVM model. On the other hand, we use some characteristics of the Gaussian kernel function and a previous result in the literature to obtain novel bounds for @c and @e hyper-parameters. The search space reductions proposed are evaluated in different regression problems from UCI and StatLib databases. All the experiments carried out applying the popular LIBSVM solver have shown that our approach reduces the SVM training time, maintaining the SVM performance similar to when the complete range in SVM parameters is considered.

Optimal switch location in mobile communication networks using hybrid genetic algorithms

The optimal positioning of switches in a mobile communication network is an important task, which can save costs and improve the performance of the network. In this paper we propose a model for establishing which are the best nodes of the network for allocating the available switches, and several hybrid genetic algorithms to solve the problem. The proposed model is based on the so-called capacitated p-median problem, which have been previously tackled in the literature. This problem can be split in two subproblems: the selection of the best set of switches, and a terminal assignment problem to evaluate each selection of switches. The hybrid genetic algorithms for solving the problem are formed by a conventional genetic algorithm, with a restricted search, and several local search heuristics. In this work we also develop novel heuristics for solving the terminal assignment problem in a fast and accurate way. Finally, we show that our novel approaches, hybridized with the genetic algorithm, outperform existing algorithms in the literature for the p-median problem.

Team formation based on group technology

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A hybrid grouping genetic algorithm for assigning students to preferred laboratory groups

This paper presents a novel application of the hybrid grouping genetic algorithm in a problem related to university timetabling. Specifically, the assignment of students to laboratory groups is tackled. This problem includes an important constraint of capacity, due to laboratories usually have a maximum number of equips or computers available, so the number of total students in a group is constrained to be equal or less than the capacity of the laboratory. In addition, our approach considers the case in which the students provide a sorted list of preferred laboratory groups, so the objective of the assignment must take this point into account. A variation of the problem in which a balanced number of students per group is required (lecturer preferences) is also studied in this paper. The performance of the approach is shown in different test problems and in a real application in a Spanish University.

An incremental-encoding evolutionary algorithm for color reduction in images

Color reduction in images is an important problem in image processing, since it is a pre-processing step in applications such as image segmentation or compression. Different methods have been proposed in the literature, several of them involving nature-inspired algorithms such as neural networks. However, not many works involving evolutionary computation techniques have been applied to this problem. This paper proposes a novel evolutionary algorithm to tackle the color reduction of RGB images. The proposed evolutionary algorithm incorporates a procedure called incremental-encoding, consisting in starting the image quantization with a small number of colors, and including additional colors in a gradual form, until reaching the final number of quantization colors. In the experiments carried out we show that the incremental-encoding evolutionary algorithm improves the performance of the standard evolutionary algorithm in this problem. Also we show that it obtains better results than several existing color reduction techniques for color quantization problems.

Teaching Advanced Features of Evolutionary Algorithms Using Japanese Puzzles

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 Alcalaacute, Madrid, Spain

Multi-parametric gaussian kernel function optimization for ε-SVMr using a genetic algorithm

In this paper we propose a novel multi-parametric kernel Support Vector Regression algorithm optimized with a genetic algorithm. The multi-parametric model and the genetic algorithm proposed are both described with detail in the paper. We also present experimental evidences of the good performance of the genetic algorithm, when compared to a standard Grid Search approach. Specifically, results in different real regression problems from public repositories have shown the good performance of the multi-parametric kernel approach both in accuracy and computation time.

A comparison of memetic algorithms for the spread spectrum radar polyphase codes design problem

This paper presents three memetic algorithms to solve the spread spectrum radar polyphase code design problem, based on Evolutionary Programming, Particle Swarm Optimization and Differential Evolution, respectively. These global search heuristics are hybridized with a gradient-based local search procedure which includes a dynamic step adaptation procedure to perform accurate and efficient local search for better solutions. We have compared the different memetic algorithms proposed in several numerical examples, and we have also demonstrated the performance of our approaches against existing approaches for this problem.