Multi-objective multi-population evolutionary procedures have become one of the most outstanding metaheuristics for solving problems characterized by the curse of dimensionality . A critical aspect of these models is the migration process, defined as the exchange of individuals between subpopulations every few iterations or generations, which has typically been adjusted according to a set of guidelines proposed more than 20 years ago, when the capacity to deal with problems was significantly less than it is today. However, the constant increase in computational power has made it possible to tackle today’s complex real-world problems of great interest more plausibly, but with larger populations than before. Against this background, this paper aims to study whether these classical recommendations are still valid today, when both the magnitude of the problems and the size of the population have increased considerably, considering how this adjustment affects the performance of the procedure. In addition, the increase in the population size , coupled with the fact that multi-objective optimization is being addressed, has led to the development of a novel elitist probabilistic migration strategy that considers only the Pareto front . The results show some interesting and unexpected conclusions, in which other issues, such as the number of subpopulations or their size, should be considered when fitting multi-population models. Furthermore, some of the previously mentioned classical recommendations may not be well-suited for high-dimensional problems.

This work was supported by project PGC2018-098813-B-C31 (Spanish "Ministerio de Ciencia, Innovacion y Universidades") , and by European Regional Development Funds (ERDF) . Funding for open access charge: Universidad de Granada/CBUA. We would like to thank Dr. Daniel Castillo-Secilla and Dr. John Q. Gan for making available to us, respectively, the lung cancer dataset and the University of Essex BCI data files.

Summary By means of the availability of mechanisms such as Dynamic Voltage and Frequency Scaling (DVFS) and heterogeneous architectures including processors with different power consumption profiles, it is possible to devise scheduling algorithms that are aware of both runtime and energy consumption in parallel programs. In this paper, we propose and evaluate a multi‐objective (more specifically, a bi‐objective) approach to distribute the workload among the processing cores in a given heterogeneous parallel CPU‐GPU architecture. The aim of this distribution may be either to save energy without increasing the running time or to reach a trade‐off among time and energy consumption. The parallel programs considered here are master‐worker evolutionary algorithms where the evaluation of the fitness function for the individuals in the population demands the most part of the computing time. As many useful bioinformatics and data mining applications exhibit this kind of parallel profile, the proposed energy‐aware approach for workload scheduling could be frequently applied.

This work presents the design of a mobile gateway for independent life and e-health support. Technologies such as OSGi and Web Services have been used to deploy several services as an example, such as bio-medical parameter monitorization, alerts, and communication with a coordination center. A complete functional service which uses context-awareness has been explained and the benefits of using Service Oriented Architecture are explained. Finally, the lessons learned of this kind of development, which involves several types of systems, data, languages, and development groups, are discussed.

Marital dissolutions, that include divorces and separations, are considered one of the most adverse events that can influence the health, life quality and welfare of the adults and infants implied. There have been several researches in the sanitary and sociological field that show how these processes of breaking a life in common can deteriorate the health in both, the physic and the psychological, aspect. This paper presents the application of machine learning methods to be able to predict if a marriage, that has started a dissolution process, will end up in a friendly agreement or it will be taken to the court. In order to accomplish this task, Radial Basis Function Neural Networks in combination with Mutual Information will be able to determine which elements should be considered to make a prediction. As the experiments will show, the methodology applied is able to classify with a high accuracy and robustness a real data base. The results could be applied in order to prevent some traumas to the people involved in the dissolution in the medical aspect and to perform a better management in the legal aspects.

Spino Cerebellar Ataxia type 2 is an autosomal dominant cerebellar hereditary ataxia with the highest prevalence in Cuba. Typical symptoms in patients of SCA2 ataxia include modifications in latency, peak velocity, and deviation in visual saccadic movements. After applying some electro-oculography based tests to both healthy and SCA2 afflicted individuals, differences in saccade morphology were found, mainly for 60° of stimulus amplitude. In this work, independent component analysis (ICA) is used to decompose the pulse and step components for the control of saccadic ocular movements, finding significant differences in time and amplitude ratio of both components. The obtained results point out the validity of the proposed methodology in order to study the effects of ataxia SCA2 in the oculomotor system.

Tooth bleaching is receiving an increasing interest by patients and dentists since it is a relatively non-invasive approach for whitening and lightening teeth. Instrument designed for tooth color measurements and visual assessment with commercial shade guides are nowadays used to evaluate the tooth color. However, the degree of color change after tooth bleaching varied substantially among studies and currently, there are no objective guidelines to predict the effectiveness of a tooth bleaching treatment. Fuzzy Logic is a well known paradigm for data modelling; their main advantage is their ability to provide an interpretable set of rules that can be later used by the scientists. However these models have the problem that the global approximation optimization can lead to a deficient rule local modelling. This work proposes a modified fuzzy model that performs a simultaneous global and local modelling. This property is reached thanks to a special partitioning of the input space in the fuzzy system. The proposed approach is used to approximate a set of color measurements taken after a bleaching treatment using the pre-bleaching measurements. The system uses as rule antecedents the colorimetric values of the VITA commercial shade guide. The expected post-bleaching colorimetric values are immediately obtained from the local models (rules) of the system thanks to the proposed modified fuzzy model. Additionally, these post-bleaching CIELAB coordinate values have been associated with VITA shades through the evaluation of their respective membership functions, approximating which VITA shades are expected after the treatment for each pre-bleaching VITA shade.

Radial basis function neural networks (RBFNNs) are well known because, among other applications, they present a good performance when approximating functions although their design still remains as a difficult task. The function approximation problem arises in the construction of a control system to optimize the process of the mineral reduction. In order to regulate the temperature of the ovens and other parameters, a module to predict the final concentration of mineral that will be obtained from the source materials is necessary. In a previous work, this problem was successfully solved by designing an RBFNN using a MultiObjective genetic algorithm (MOGA). However, the more samples are obtained from the system, the more difficult it becomes to design the RBFNN due to the high dimensionality of the problem. Therefore, a new algorithm that addresses the dimensionality reduction has been developed, allowing to obtain more accurate RBFNNs, deciding which input parameters must be considered. Another important element incorporated in the algorithm is the concept of fuzzy dominance, the algorithm, when performing the sorting of the population dividing it in subsets of non-dominated individuals, uses a fuzzy criteria to decide if an individual dominates another. As the experimental results will show, the new version of the algorithm generates RBFNNs with smaller approximation errors and less complexity due to the reduction in the number of input variables and neurons.

Radial basis function neural networks (RBFNNs) have been applied to solve problems of classification, function approximation and time series prediction. In the design of an RBFNN it is necessary to set the values for the positions of the centers and the radii for each RBF. In the literature it is usually performed an initialization step to set the positions of the centers and, once they are placed, the radii are calculated using a heuristic. In this paper, a new algorithm to set the value of those two parameters is presented. This new algorithm uses a supervised learning in such a way that the position of the centers will be constrained by the output of the function to be approximated. Since each center represents a neuron that is activated by the input vectors, the radii are initialized using the center's positions and their activation grades. In this way, the calculation of the radii is also influenced by the output of the target function, not like in other heuristics where only the positions of the centers or the input vectors are considered. As the experiments show, the new algorithm outperforms other algorithms previously used for this problem.

The logarithmic image processing model (LIP) is a robust mathematical framework, which, among other benefits, behaves invariantly to illumination changes. This paper presents, for the first time, two general formulations of the 2-D convolution of separable kernels under the LIP paradigm. Although both formulations are mathematically equivalent, one of them has been designed avoiding the operations which are computationally expensive in current computers. Therefore, this fast LIP convolution method allows to obtain significant speedups and is more adequate for real-time processing. In order to support these statements, some experimental results are shown in Section V.

The goal of this paper is to achieve real time control of a monotonic system which, in general, may be non-linear and whose differential equations are unknown. We assume that there is no model of the plant available so there cannot be any off-line pre-training of the main controller parameters. We propose a both adaptive and self-learning algorithm capable of starting from a "void" fuzzy controller and, in real time, optimizing the fuzzy controller's rules (both antecedents and consequents) in order to translate the state of the plant to the desired value in the shortest possible time.

In this paper, the multigrid-based fuzzy system (MGFS) approach is applied for the CATS time series prediction benchmark. The MGFS architecture overcomes the problem inherent to all grid-based fuzzy systems when dealing with high dimensional input data, thus keeping low computational cost and high performance. A greedy algorithm for MGFS structure identification allows to perform the input variable selection for the time series prediction problem, while identifying the pseudo-optimal architecture according to the provided dataset.

A novel approach to achieve real-time global learning in fuzzy controllers is proposed. Both the rule consequents and the membership functions defined in the premises of the fuzzy rules are tuned using a one-step algorithm, which is capable of controlling nonlinear plants with no prior offline training. Direct control is achieved by means of two auxiliary systems: The first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input-output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed, avoiding overfitting.

This paper presents a multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs. The procedure allows the application of heuristics to improve the solution of the problem at hand by including some new genetic operators in the evolutionary process. These new operators are based on two well-known matrix transformations: singular value decomposition (SVD) and orthogonal least squares (OLS), which have been used to define new mutation operators that produce local or global modifications in the radial basis functions (RBFs) of the networks (the individuals in the population in the evolutionary procedure). After analyzing the efficiency of the different operators, we have shown that the global mutation operators yield an improved procedure to adjust the parameters of the RBFNNs.

This paper presents an innovative approach to self-adaptation of the structure of a fuzzy controller in real time. Without any off-line pretraining, the algorithm achieves very high control performance through the iteration of a three-stage algorithm. In the first stage, coarse tuning of the fuzzy rules (both rule consequents and membership functions of the premises) is accomplished using the sign of the dependency of the plant output with respect to the control signal and an overall analysis of the main operating regions. In stage two, fine tuning of the fuzzy rules is achieved based on the controller output error using a gradient-based method. Finally, the third stage is responsible of modifying the structure of the fuzzy controller, proposing that input variable which should get a new membership function in order to improve the control policy in an optimum way.

The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. There are numerous approaches to the issue of parameter optimization within a fixed fuzzy system structure but no reliable method to obtain the optimal topology of the fuzzy system from a set of input-output data. This paper presents a reliable method to obtain the structure of a complete rule-based fuzzy system for a specific approximation accuracy of the training data, i.e., it can decide which input variables must be taken into account in the fuzzy system and how many membership functions (MFs) are needed in every selected input variable in order to reach the approximation target with the minimum number of parameters.

To date, clustering techniques have always been oriented to solve classification and pattern recognition problems. However, some authors have applied them unchanged to construct initial models for function approximators. Nevertheless, classification and function approximation problems present quite different objectives. Therefore it is necessary to design new clustering algorithms specialized in the problem of function approximation. This paper presents a new clustering technique, specially designed for function. approximation problems, which improves the performance of the approximator system obtained, compared with other models derived from traditional classification oriented clustering algorithms and input-output clustering techniques.

The Web newspaper pagination problem consists of optimizing the layout of a set of articles extracted from several Web newspapers and sending it to the user as the result of a previous query. This layout should be organized in columns, as in real newspapers, and should be adapted to the client Web browser configuration in real time. This paper presents an approach to the problem based on simulated annealing (SA) that solves the problem on-line, adapts itself to the client's computer configuration, and supports articles with different widths.

Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover, mutation, and selection operators) manually. Nevertheless, when GA applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of a GA. The purpose of this study was to analyze the dynamics of GAs when confronted with modifications to the principal parameters that define them, taking into account the two main characteristics of GAs; their capacity for exploration and exploitation. Therefore, the dynamics of GAs have been analyzed from two viewpoints. The first is to study the best solution found by the system, i.e., to observe its capacity to obtain a local or global optimum. The second viewpoint is the diversity within the population of GAs; to examine this, the average fitness was calculated. The relevancy and relative importance of the parameters involved in GA design are investigated by using a powerful statistical tool, the analysis of the variance (ANOVA).

An enhanced self-organizing controller (E-SOC) is presented to achieve real time global learning in fuzzy controllers. Direct control is achieved by means of two auxiliary systems: the first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input/output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed.

A new approach is presented to fine tune a fuzzy controller based on very limited information on the nonlinear plant to be controlled. Without any off-line pre-training, the algorithm achieves very high control performance through a two-stage algorithm. In the first stage, coarse tuning of the fuzzy rules (both rule consequents and membership functions of the premises) is accomplished in real time using a small amount of information about the plant and an overall analysis of the main operating regions. In stage two, fine tuning of the fuzzy rules is achieved based on the controller output error using a gradient-based method.

This paper presents a reliable method to obtain the structure of a complete rule-based fuzzy system for a specific approximation accuracy of the training data, i.e. it can decide which input variables must be taken into account in the fuzzy system and how many membership functions are needed in every selected input variable in order to reach the approximation target with the minimum number of parameters.

This paper proposes a framework for constructing and training a radial basis function (RBF) neural network, which is an example of fuzzy system. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit (rule) and also to detect and remove inactive units. The structure of the gaussian functions (membership functions) is modified using a pseudo-Gaussian function (PG) in which two sealing parameters /spl sigma/ are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. Other important characteristics of the proposed neural system is that instead of using a single parameter for the output weights, these are functions of the input variables which leads to a significant reduction in the number of hidden units compared with the classical RBF network Finally, we examine the result of applying the proposed algorithm to time series prediction.

In this paper we propose a genetic algorithm (GA) that is capable of simultaneously optimizing the structure of the system and tuning the parameters that define the fuzzy system. For this purpose, we use the concept of multiple-deme GAs, in which several populations with different structures (number of input variables) evolve and compete with each other. In each of these populations, the element also has different numbers of membership functions in the input spaces and different numbers of rules. Instead of the normal coding system used to represent a fuzzy system, in which all the parameters are represented in vector form, we performed coding by means of multidimensional matrices, in which the elements are real-valued numbers, rather than the traditional binary or gray coding.

Describes a structure to create a RBF neural network. This structure has 4 main characteristics. The first one is that the special RBF network architecture uses regression weights to replace the constant weights normally used. These regression weights are assumed to be functions of input variables. The second characteristic is the normalization of the activation of the hidden neurons (weighted average) before aggregating the activations, which, as observed by various authors, produces better results than the classical weighted sum architecture. The third aspect is that a new type of nonlinear function is proposed, the pseudo-gaussian function (PGBF). With this, the neural system gains flexibility, as the neurons possess an activation field that does not necessarily have to be symmetric with respect to the centre or to the location of the neuron in the input space. In addition to this new structure, we propose, as the fourth and final feature, a sequential learning algorithm, which is able to adapt the structure of the network, with this, it is possible to create new hidden units and also to detect and remove inactive units.

We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.

In this paper, a systematic design is proposed to determine fuzzy system structure and learning its parameters, from a set of given training examples. In particular, two fundamental problems concerning fuzzy system modeling are addressed: 1) fuzzy rule parameter optimization and 2) the identification of system structure (i.e., the number of membership functions and fuzzy rules). A four-step approach to build a fuzzy system automatically is presented: Step 1 directly obtains the optimum fuzzy rules for a given membership function configuration. Step 2 optimizes the allocation of the membership functions and the conclusion of the rules, in order to achieve a better approximation. Step 3 determines a new and more suitable topology with the information derived from the approximation error distribution; it decides which variables should increase the number of membership functions. Finally, Step 4 determines which structure should be selected to approximate the function, from the possible configurations provided by the algorithm in the three previous steps. The results of applying this method to the problem of function approximation are presented and then compared with other methodologies proposed in the bibliography.