University of Granada, Spain, Research Center for ICT
Antonio D. Masegosa was born in Granada (Spain) on February 2nd, 1982. He took his University degree in Computer Engineering in 2005 and his PhD in Computer Sciences in 2010, both from the University of Granada, Spain. From June 2010 he has been a post-doc researcher at the Research Center for ICT of the University of Granada. He has published three books and more than 20 papers in leading scientific journals and in both international and national conferences. He is member of the editorial board of the International Journal of Swarm Intelligence and Evolutionary Computation (OMICS) and the program committee of international conferences as IEEE CEC, ECAL or NICSO. He has served as reviewer in international journals as Information Sciences, NeuroComputing and Memetic Computing and international conferences as GECCO. His main research interests are: Intelligent Systems, Soft Computing, Cooperative Hybrid Metaheuristics, Dynamic Optimization Problems, Fuzzy Systems, and Scenario Planning among others.
University of Granada, Spain, Research Center for ICT
University of Granada, Spain, Department of Computer Sciences and Artificial Intelligence
Ph.D. in Computer Science
University of Granada, Spain
Master in Soft Computing and Intelligent Systems
University of Granada
Computer Engineering Degree
University of Granada, Spain
I am a member of the Models of Decision and Optimization Research Group (MODO)
My research interests are mainly focused on Soft Computing and optimization. One of my research topics are cooperative hybrid metaheuristics. These methods consist of many parallel cooperating agents, where each agent accomplishes a search in a solution space by means of a metaheuristic. I have applied these algorithms to different fields as: hub location, maximum satisfiability problems, continuous optimization, simultaneous resolution of instances, systems biology, etc.
Another research topic is the design of metaheuristics to solve Dynamic Optimization Problems. Apart from the above mentioned cooperative hybrid metaheuristics, I am currently studying the use of Algorithm Portfolios to deal with these problems.
I am also interested in the application of Soft Computing techniques, especially fuzzy sets and metaheuristics, to solve real-world problems in uncertain and imprecise environments. The main problems I have work with are scenario planning, scheduling and covering problems for emergency systems.
Virgilio Cruz (Current co-advisor together with D. Pelta)
Topic: Fuzzy techniques for handling of uncertainty and vagueness in covering location problems
University of Granada
Jenny Fajardo (Current co-advisor together with D. Pelta)
Topic: Algorithm Portfolios for solving Dynamic Optimization Problems
University of Granada- CUJAE (Havanna, Cuba)
Virgilio Cruz (Co-mentored together with D. Pelta)
Title: Intelligent Systems of social and healthcare attention: a tool for the design and validation of deployment of services
University of Granada
Federico Rutolo (Co-mentored together with J.L. Verdegay)
Title: Study and development of cooperative strategies for Systems Biology problems
University of Granada - University of Rome
You can also find a complete list of my publications in my Google Scholar profile
Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation.
This book is a collection of research works presented in the VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) held in Canterbury, UK. Previous editions of NICSO were held in Granada, Spain (2006 & 2010), Acireale, Italy (2007), Tenerife, Spain (2008), and Cluj-Napoca, Romania (2011). NICSO 2013 and this book provides a place where state-of-the-art research, latest ideas and emerging areas of nature inspired cooperative strategies for problem solving are vigorously discussed and exchanged among the scientific community. The breadth and variety of articles in this book report on nature inspired methods and applications such as Swarm Intelligence, Hyper-heuristics, Evolutionary Algorithms, Cellular Automata, Artificial Bee Colony, Dynamic Optimization, Support Vector Machines, Multi-Agent Systems, Ant Clustering, Evolutionary Design Optimisation, Game Theory and other several Cooperation Models.
The evolution of soft computing applications has offered a multitude of methodologies and techniques that are useful in facilitating new ways to address practical and real scenarios in a variety of fields.
Exploring Innovative and Successful Applications of Soft Computing highlights the applications and conclusions associated with soft computing in different technological environments. Providing potential results based on new trends in the development of these services, this book aims to be a reference source for researchers, practitioners, and students interested in the most successful soft computing methods applied to recent problems
This book is a PhD dissertation which focuses on the study, design, development and application of centralised cooperative strategies for optimisation problems. These models consist of a set of parallel cooperating agents, where each agent carries out a search in a solution space sharing information with the rest of the agents. Many studies have shown that this cooperation leads to more efficient and effective strategies. The authors start describing the most known trajectory and population-based metaheuristics and introduce the cooperative strategies. The core of this book is devoted to the following topics: analysis of the composition and the cooperation scheme, hybridization with Reactive Search ideas, application of centralised cooperative strategies to Dynamic Optimisation Problems and resolution of sets of instances by a cooperative method. The book is addressed to students or researchers in the fields of Intelligent Systems, Soft Computing, Optimisation Techniques and, especially, Hybrid Metaheuristics.
An open question that arises in the design of adaptive schemes for Dynamic Optimization Problems consists on deciding what to do with the knowledge acquired once a change in the environment is detected: forget it or use it in subsequent changes? In this work, the knowledge is associated with the selection probability of two local search operators in the Adaptive Hill Climbing Memetic Algorithm. When a problem change is detected, those probability values can be restarted or maintained. The experiments performed over five binary coded problems (for a total of 140 different scenarios) clearly show that keeping the information is better than forgetting it.
One of the methodologies more used to accomplish prospective analysis is the scenario method. The first stage of this method is the so called structural analysis and aims to determine the most important variables of a system. Despite being widely used, structural analysis still presents some shortcomings, mainly due to the vagueness of the information used in this process. In this sense, the application of Soft Computing to structural analysis can contribute to reduce the impact of these problems by providing more interpretable and robust models. With this in mind, we present a methodology for structural analysis based on computing with words techniques to properly address vagueness and increase the interpretability. The method has been applied to a real problem with encouraging results.
In this work we discuss to what extent and in what contexts the use of knowledge discovery techniques can improve the performance of cooperative strategies for optimization. The study is approached over two different cases study that differs in terms of the definition of the initial cooperative strategy, the problem chosen as test bed (Uncapacitated Single Allocation p HubMedian and knapsack problems) and the number of instances available for applying data mining. The results obtained show that this techniques can lead to an improvement of the cooperatives strategies as long as the application context fulfils certain characteristics.
Cooperative strategies are search techniques composed by a set of individual methods (solvers) that, through information exchange, cooperate to solve an optimization problem. In this paper, we focus on the composition of such set and we analyze the results of a cooperative strategy when the solvers in the set are equal (homogeneous) or different (heterogeneous). Using the Uncapacitated Single Allocation p-Hub Median Problem as test bed we found that taking copies of the same solver and adding cooperation, the results are better than using an isolated solver. Regarding using different solvers, the cooperative heterogeneous scheme is usually better than the best performing isolated solver search (which usually changes in terms of the instance being solved). In terms of heterogeneous vs. homogeneous composition of the cooperative strategy, an advantage in using the former scheme can be observed.
Cooperative strategies and reactive search are very promising techniques for solving hard optimization problems, since they reduce human intervention required to set up a method when the resolution of an unknown instance is needed. However, as far as we know, a hybrid between both techniques has not yet been proposed in the literature. In this work, we show how reactive search principles can be incorporated into a simple rule-driven centralised cooperative strategy. The proposed method has been tested on the Uncapacitated Single Allocation p-Hub Median Problem, obtaining promising results.
Most of the adaptive metaheuristics face the resolution of an instance from scratch, without considering previous runs. Basing on the idea that the computa- tional effort done and knowledge gained when solving an instance should be use to solve similar ones, we present a new metaheuristic strategy that permits the simul- taneous solution of a group of instances. The strategy is based on a set of adaptive operators that works on several sets of solutions belonging to different problem in- stances. The method has been tested on MAX-SAT with sets of various instances obtaining promising results.
Since their first appearance in 1997 in the prestigious journal Science, Algorithm Portfolios have become a popular approach to solve static problems. Nevertheless and despite that success, they have not received much attention in Dynamic Optimization Problems (DOPs). In this work, we aim at showing these methods as a powerful tool to solve combinatorial DOPs. To this end, we propose a new Algorithm Portfolio for this type of problems that incorporates a learning scheme to select, among the metaheuristics that compose it, the most appropriate solver/s for each problem, configuration and search stage. This method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad, Deceptive and Knapsack) and compared versus two reference algorithms for these problems, AHMA and SORIGA. The results showed the importance of a good design of the learning scheme, the superiority of the Algorithm Portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its performance versus the reference algorithms.
Scenario Planning helps explore how the possible futures may look like and establishing plans to deal with them, something essential for any company, institution or country that wants to be competitive in this globalize world. In this context, Cross Impact Analysis is one of the most used methods to study the possible futures or scenarios by identifying the system’s variables and the role they play in it. In this paper, we focus on the method called MICMAC (Impact Matrix Cross-Reference Multiplication Applied to a Classification), for which we propose a new version based on Computing with Words techniques and fuzzy sets, namely Fuzzy Linguistic MICMAC (FLMICMAC). The new method allows linguistic assessment of the mutual influence between variables, captures and handles the vagueness of these assessments, expresses the results linguistically, provides information in absolute terms and incorporates two new ways to visualize the results. Our proposal has been applied to a real case study and the results have been compared to the original MICMAC, showing the superiority of FLMICMAC as it gives more robust, accurate, complete and easier to interpret information, which can be very useful for a better understanding of the system.
The best performing methods for Dynamic Optimization Problems (DOPs) are usually based on a set of agents that can have different complexity (like solutions in Evolutionary Algorithms, particles in Particle Swarm Optimization, or metaheuristics in hybrid cooperative strategies). While methods based on low-complexity agents are widely applied in DOPs, the use of more “intelligent” agents has rarely been explored. This work focuses on this topic and more specifically on the use of cooperative strategies composed by trajectory-based search agents for DOPs. Within this context, we analyze the influence of the number of agents (cardinality) and their neighborhood sampling strategy on the performance of these methods. Using a low number of agents with distinct neighborhood sampling strategies shows the best results. This method is then compared versus state-of-the-art algorithms using as test bed the well-known Moving Peaks Benchmark and dynamic versions of the Ackley's, Griewank's and Rastrigin's functions. The results show that this configuration of the cooperative strategy is competitive with respect to the state-of-the-art methods.
The necessity of developing high-performance resolution methods for continuous optimisation problems has given rise to the emergence of cooperative strategies which combine different self-contained metaheuristics that exchange information among them. However, the majority of the proposals found in the literature make use of population-based algorithms and/or employ a cooperation scheme with a pipeline or decentralised information flow. In this work we proposed a centralised cooperative strategy, where a set of trajectory-based methods are controlled by a rule-driven coordinator. In this context, we also present a new analysis that allows to study the behaviour induced by a determined type of cooperation in the strategy. A comprehensive experimentation has been accomplished over CEC2005 and CEC2008 benchmarks in order to assess the performance of the method with different cooperation schemes. The results show that these cooperation schemes, apart from having a different performance, lead the strategy to distinct exploration and exploitation patterns. In addition, the proposed method presents competitive results with respect to state-of-the-art algorithms for both benchmarks.
This work presents a study on the performance of several algorithms on different continuous dynamic optimization problems. Eight algorithms have been used: SORIGA (an Evolutionary Algorithm), an agents-based algorithm, the mQSO (a widely used multi-population PSO) as well as three heuristic-rule-based variations of it, and two trajectory-based cooperative strategies. The algorithms have been tested on the Moving Peaks Benchmark and the dynamic version of the Ackley, Griewank and Rastrigin functions. For each problem, a wide variety of configuration variations have been used, emphasizing the influence of dynamism, and using a full-factorial experimental design. The results give an interesting overview of the properties of the algorithms and their applicability, and provide useful hints to face new problems of this type with the best algorithmic approach. Additionally, a recently introduced methodology for comparing a high number of experimental results in a graphical way is used.
Optimization in dynamic environments is a very active and important area which tackles problems that change with time (as most real-world problems do). In this paper we present a new centralized cooperative strategy based on trajectory methods (tabu search) for solving Dynamic Optimization Problems (DOPs). Two additional methods are included for comparison purposes. The first method is a Particle Swarm Optimization variant with multiple swarms and different types of particles where there exists an implicit cooperation within each swarm and competition among different swarms. The second method is an explicit decentralized cooperation scheme where multiple agents cooperate to improve a grid of solutions. The main goals are: firstly, to assess the possibilities of trajectory methods in the context of DOPs, where populational methods have traditionally been the recommended option; and secondly, to draw attention on explicitly including cooperation schemes in methods for DOPs. The results show how the proposed strategy can consistently outperform the results of the two other methods.
Having in mind the idea that the computational effort and knowledge gained while solving a problem’s instance should be used to solve other ones, we present a new strategy that allows to take advantage of both aspects. The strategy is based on a set of operators and a basic learning process that is fed up with the information obtained while solving several instances. The output of the learning process is an adjustment of the operators. The instances can be managed sequentially or simultaneously by the strategy, thus varying the information available for the learning process. The method has been tested on different SAT instance classes and the results confirm that (a) the usefulness of the learning process and (b) that embedding problem specific algorithms into our strategy, instances can be solved faster than applying these algorithms instance by instance.
Optimization problems are ubiquitous in our daily lives and one way to cope with them is using cooperative optimization systems that allow to obtain good enough, fast enough, and cheap enough solutions. From a practical point of view, the design and the analysis of such systems are complex tasks. In this work, an integrated system (DACOS) for helping in the design and analysis of cooperative, centralized optimization systems is presented. Also, the methodology used for the creation of DACOS (mainly, the use of software modeling) is described in detail. This may also be useful for researchers who want to build up their own system for their particular needs. DACOS has been developed using the Eclipse developing framework, which, among other advantages, is also able to automatically generate source code. Finally, a practical case of use is presented: the application of DACOS to the configuration and analysis of a cooperative strategy on a location problem.
Optimization-based decision support systems (DSSs) are an interesting and important area among the many classes of decision support systems. This paper presents SiGMA, a generic core to build Optimization-based DSSs that tries to be as generic as possible on the on-line addition and use of solvers while preserving the maximum functionality on the Analysis stage that this criterion allows. SiGMA serves as a framework to build more complex DSSs where problem specific knowledge can be used to improve the functionality available at the Formulation and Analysis stages. Two application examples from different domains are also presented: SiGMAPhub and SiGMAProt. These applications include additional analysis capabilities for the p-hub and the protein structure comparison problems, respectively.
La gestión de la incertidumbre en los problemas de localización de cobertura máxima (PLCM) es muy importante debido a la naturaleza imprecisa de algunos elementos de estos problemas en el mundo real. La demanda generada en los nodos, la distancia entre nodos, la disponibilidad del servicio y el radio de cobertura son los parámetros que comúnmente se pueden considerar inciertos. Los enfoques probabilísticos y los enfoques difusos han sido los más utilizados para modelar la incertidumbre en el PLCM, pero los de corte difuso no están estructurados o sistematizados. Por eso en este artículo se presenta una revisión de los trabajos que han abordado el PLCM con incertidumbre de tipo difuso, como paso previo para el planteamiento sistemático de modelos y soluciones para el mismo.
Una pregunta que surge en el diseño de esquemas de aprendizaje para Problemas Dinámicos de Optimización consiste en decidir qué hacer con el conocimiento que se ha adquirido una vez que se produce un cambio: olvidarlo o utilizarlo en los cambios posteriores. En este trabajo, intentamos arrojar a la luz sobre este asunto usando el método Adaptive Hill Climbing Memetic Algorithm y el problema de la Mochila como banco de pruebas. Los resultados obtenidos muestran que la respuesta a la pregunta planteada depende de la estructura de la instancia y justica la necesidad de realizar estudios más completos.
En el mundo moderno, las grandes empresas necesitan predecir los cambios en el mercado y anticiparse a ellos tomando las decisiones más adecuadas en el presente. Entre las herramientas especializadas para ello se encuentra la Planificación de Escenarios. Una de las técnicas usadas en Planificación de Escenarios es el Análisis Morfológico, cuya finalidad es la generación sistemática y evaluación de todas las posibles combinaciones de valores que las variables objeto de estudio pueden tomar, descartando aquellas incompatibles en la práctica. Cada combinación representa un posible escenario futuro, es decir, una situación plausible que puede llegar a darse. La variante conocida como MORPHOL evalúa un escenario mediante la probabilidad de que ocurra finalmente. Aquí se revisa una mejora publicada recientemente que considera otros criterios distintos a la probabilidad para evaluar un escenario usando valores lingüísticos. Además, se proponen mejoras futuras y nuevas líneas que están actualmente siendo investigadas, como clustering de los escenarios generados y obtención de descripciones lingüísticas de cada escenario, con ejemplos sobre un caso de estudio real.
Companies that want to be competitive must make use of good practices to anticipate the future by analyzing the possible effects of today's decisions on their own long-term development. Scenario planning is among the most extended approaches to accomplish this. One of the techniques often used in scenario planning is Morphological Analysis, which aims to explore the space of feasible futures in a systematic way by analyzing all the combinations of the possible states of the variable that compose the system under study. Each of this combinations represent a possible future scenario. This work focuses on a particular variant known as the MORPHOL method, in which every scenario is evaluated in terms of the probability it eventually arises. This is computed using the marginal probability estimates of the hypothetical variables' states involved in the scenario, which are given by human experts. This method presents two drawbacks: first, the probabilities have to be expressed by numerical values which makes difficult its estimation by humans and does not capture its uncertainty; and second, it examines the scenarios basing only on their probability, thus it may ignore scenarios that are interesting but not the very probable. In order to ease the experts' task and capture their opinions in a better way, we introduce Computing with Words techniques. For solving the second shortcoming, we apply Multi-criteria Decision Making to uncover good scenarios according to several criteria jointly including probability. The result is a novel linguistic multi-criteria method for morphological analysis that has been successfully applied to a real problem and thus deserves further research.
La prospectiva tecnológica puede definirse como un conjunto de estudios que se llevan a cabo con el fin de anticipar cuál va a ser el futuro en una determinada área. Una metodología ampliamente utilizada para ello es el Método de los Escenarios de Godet, que incluye un módulo para realizar el conocido como análisis estructural (MIC-MAC). Su finalidad es determinar las variables más importantes en el sistema que se va a estudiar, tomando como entradas las relaciones de influencia existentes entre ellas, las cuales vienen cuantificadas numéricamente por un grupo de expertos en forma de matriz. Proponemos una extensión difusa de este método que permite a los expertos juzgar las relaciones mediante etiquetas lingüísticas y obtener resultados más fácilmente intepretables. Esto es una importante ventaja sobre el método tradicional, ya que en este tipo de estudios, la interpretabilidad de las salidas y los resultados intermedios es un aspecto primordial. Se ha realizado una implementación en Java de nuestro procedimiento difuso con una interfaz web accesible públicamente, y que demuestra la facilidad de uso del enfoque planteado. Los primeros resultados son interesantes desde el punto de vista práctico.
Developing predictive models is one of the key issues in Systems Biology. A critical problems that arises when these models are built is the parameter estimation. The calibration of these nonlinear dynamic models is stated as a nonlinear programming problems (NLP) and its resolution is usually complex due to the frequent ill-conditioning and multimodality of the majority of these problems. For that reason, the use of hybrid stochastic optimization methods has received an increasing interest in recent years. In this work we present a new hybrid method for parameter estimation in Systems Biology. This proposal consists on a set of DE algorithms that cooperate among them through a centralised scheme in which a coordinator controls their behavior by means of a rule system. The comparison with state-of-the-art methods shows the better performance of this cooperative strategy when the complexity of the instances is increased.
Technology foresight deals with the necessity of anticipating the future to better adapt to new situations regarding innovations that directly affect business world. One widely spread methodology in technology foresight is Godet's Scenario Method, which includes a module (MICMAC) performing the so-called structural analysis. The goal of the structural analysis is to identify the most important variables in a system. To this end, it makes use of an influence matrix that describes the relations between the variables. This information is usually given by experts based on their own knowledge and experience. However, some of the information of the influence matrix may contain errors due to the subjective nature of the criteria and opinions of the experts. Here we propose a new analysis that follows a multi-objective approach and allows to measure the sensibility of the model versus possible errors at the input. The well-known NSGA-II algorithm has been used as a solver. The results are encouraging and deserve further investigation.
Dentro de los distintos tipos de metaheurísticas híbridas, las estrategias cooperativas se presentan como una de las alternativas más prometedoras. Este tipo de métodos consisten en un conjunto de algoritmos que exploran el espacio de búsqueda de forma simultánea mientras intercambian información entre ellos. Con el objetivo de profundizar en el estudio de dichos métodos y utilizando el problema del p-hub mediano, en este trabajo analizamos como afecta al comportamiento de la estrategia cooperativa el hecho de cambiar de forma notoria la configuración de las metaheurísticas que la componen.
Optimisation in dynamic environments is a very active and important area which tackles problems that change with time (as most real-world problems do). The possibility to use a new centralised cooperative strategy based on trajectory methods (tabu search) for solving Dynamic Optimisation Problems (DOPs) was previously introduced showing good results against state of the art methods like the Particle Swarm Optimisation (PSO) variant with multiple swarms and different types of particles. The analysis of this previous work are further extended here by exploring more possibilities for the cooperation rules used in the strategy. The results show that different classes of cooperation can lead to quite different results, some of them greatly outperforming the previous ones.
Metaheuristics are excellent tools for addressing hard combinatorial optimization problems. Given a limited quantity of time and space resources, heuristic algo- rithms are very effective in providing good quality solutions. However, among the open problems in this research field, we want to the outline following ones:
The first issue can be handled by cooperative strategies , where a set of potentially good heuristics for the optimization problem are executed in parallel, sharing information during the run. The second problem is successfully addressed by Reactive strategies , and the use of sub-symbolic machine learning to automate the parameter tuning process, making it an integral part of the algorithm. In , a centralized cooperative strategy is presented where a coordinator controls a set of heuristics by means of a Fuzzy rule base. The aim of this paper is to investigate the effectiveness of a coordinator driven by Reactive rules. An empirical comparison between both rules is provided using the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP)  as example.
La mayoría de las metaheurísticas con mecanismos de auto-adaptación que se han propuesto en la literatura, afrontan cada resolución desde el principio, sin tener en cuenta lo aprendido durante otras búsquedas. Basándonos en la idea de que el esfuerzo computacional realizado para la resolución de una instancia debería ser aprovechado en la solución de instancias similares, presentamos un nuevo modelo que permite la solución simultánea de un grupo de instancias. Este modelo contiene un conjunto de agentes que operan sobre un espacio de búsqueda. Presenta un esquema multi-nivel de 2 capas que controla jerárquicamente la autoadaptación de los parámetros de los operadores. Este nuevo método ha sido aplicado al problema MAX-SAT con conjuntos de varias instancias obteniéndose unos resultados prometedores.
This intelligent system was developed in collaboration with a local company called Gobile, as a part of the joint innovation project i-APUS that aimed at implementing a software platform for police forces (subject to a "non-disclosure agreement"). Its function was the design of patrol areas for police forces in urban regions. Given an incident prediction map (provided by other software module of the i-APUS project) and the available resources (number of patrols, vehicle type, etc.) the intelligent system defines the patrol areas that maximize the expected percentage of incidents attended before a predefined time threshold. The development entailed the next aspects: modelling of the problem elements (patrols, incidents, locations, patrol areas, etc.); definition of the optimization model as a Maximum Expected Covering Location Problem; design of a metaheuristic to solve the underlying maximization problem. I was in charge of the whole design and implementation in Java of this intelligent system.
This intelligent system was developed in collaboration with a local company called Gobile, as a part of the joint innovation project i-APUS that aimed at implementing a software platform for police forces (subject to a "non-disclosure agreement"). The objective of this intelligent system for scheduling and rostering of police forces consisted on finding the assignment of polices to shifts that better fitted a predefined set of requirements (numbers of polices needed, skills, etc.) and constraints (maximum number of working hours, minimum resting time after a night shift, etc.). The development entailed the next aspects: modelling of the requirements and constraints by an XML schema; definition of the search model as a constraint satisfaction problem; and the design of a metaheuristic to solve the underlying optimization problem. I was in charge of the whole design and implementation in Java of this intelligent system.
Degree in Information and Communication. 1st year. University of Granada. 30 Hours.
Degree in Computer Engineering. 1st year. University of Granada. 32 Hours..
I would be happy to talk to you if you need my assistance in your research or your bussiness. I would be also glad to discuss with you any issue related to my research or provide you further material (source code of the software, manuscript, etc.)
You can find me at my office located at the Research Center for ICT of the University of Granada. Please, contact me to fix an appointment
The full address is:Research Center for ICT.