L'objectiu principal del grup és avançar en les metodologies que conformen l'àrea de la infor-màtica tova (Soft Computing), així com investigar possibles hibridacions d'aquestes amb l'ob-jectiu de millorar-ne el rendiment i la fiabilitat. També és un objectiu important aplicar les metodologies desenvolupades a problemes reals en àrees com la medicina, ecologia, farmacoprote-òmica, e-learning, etc. El grup ha publicat més de 150 articles de revista, més de 300 articles en congressos, 50 capítols de llibres i 60 reports de recerca.

The term "soft computing" was coined in the nineties, and it describes the combined use of a variety of computational approaches that have been developed over the last few decades, which include but are not limited to fuzzy systems, neural networks and evolutionary algorithms.

Despite their obvious differences, a common trait of these fields is the abandonment of binary logic, static analytical models, rigid classifications and deterministic searches. In an ideal problem description, the systems that require modelling and/or control would be defined completely and precisely. In this case, formal reasoning systems can be used to associate Boolean values to state descriptions or physical systems' behaviour. Nevertheless, when tackling real-world problems, it is not unusual to find them incompletely or badly defined, which makes it difficult to model them and requires large search spaces. As a result, precise models, should they exist, might turn out to be impractical and/or costly. Usually, the relevant information that is available is presented either as empirical, a priori knowledge or as input-output instance descriptions of the systems' behaviour. This makes the use of approximate reasoning systems necessary, as they can flexibly cope with such far-from-perfect information.

The main goal of the SOCO group is to make progress in the state-of-the-art development of soft-computing methodologies, as well as to research their possible hybridisation in order to improve their robustness and efficacy. The SOCO group is currently working on the following research lines:

* Feature selection and dimensionality reduction

* Fuzzy systems (Fuzzy Inductive Reasoning, FIR)

* Artificial neural networks (feed-forward, recurrent, heterogeneous)

* Unsupervised probabilistic models

* Genetic algorithms and evolutionary strategies

* Pattern recognition and computer vision

* Hybrid soft-computing methods, including the following:

- Neural networks and support vector machines

- Fuzzy Inductive Reasoning and simulated annealing

- Fuzzy Inductive Reasoning and genetic algorithms

- Frequency selection for neural networks

- Cooperation of local experts for inductive reasoning

- Incremental construction of hybrid recurrent neural networks

The application of these methodologies to real-world problems is also one of the group's goals. The group has carried out research in the following areas of application:

* Medical (human central nervous system, cancer prediction, diagnosis, cognitive neuroscience, etc.)

* Biological (growth of white shrimp)

* Ecological (analysis of pollutant concentration in urban areas and ecological status modelling of streams)

The term "soft computing" was coined in the nineties, and it describes the combined use of a variety of computational approaches that have been developed over the last few decades, which include but are not limited to fuzzy systems, neural networks and evolutionary algorithms.

Despite their obvious differences, a common trait of these fields is the abandonment of binary logic, static analytical models, rigid classifications and deterministic searches. In an ideal problem description, the systems that require modelling and/or control would be defined completely and precisely. In this case, formal reasoning systems can be used to associate Boolean values to state descriptions or physical systems' behaviour. Nevertheless, when tackling real-world problems, it is not unusual to find them incompletely or badly defined, which makes it difficult to model them and requires large search spaces. As a result, precise models, should they exist, might turn out to be impractical and/or costly. Usually, the relevant information that is available is presented either as empirical, a priori knowledge or as input-output instance descriptions of the systems' behaviour. This makes the use of approximate reasoning systems necessary, as they can flexibly cope with such far-from-perfect information.

The main goal of the SOCO group is to make progress in the state-of-the-art development of soft-computing methodologies, as well as to research their possible hybridisation in order to improve their robustness and efficacy. The SOCO group is currently working on the following research lines:

* Feature selection and dimensionality reduction

* Fuzzy systems (Fuzzy Inductive Reasoning, FIR)

* Artificial neural networks (feed-forward, recurrent, heterogeneous)

* Unsupervised probabilistic models

* Genetic algorithms and evolutionary strategies

* Pattern recognition and computer vision

* Hybrid soft-computing methods, including the following:

- Neural networks and support vector machines

- Fuzzy Inductive Reasoning and simulated annealing

- Fuzzy Inductive Reasoning and genetic algorithms

- Frequency selection for neural networks

- Cooperation of local experts for inductive reasoning

- Incremental construction of hybrid recurrent neural networks

The application of these methodologies to real-world problems is also one of the group's goals. The group has carried out research in the following areas of application:

* Medical (human central nervous system, cancer prediction, diagnosis, cognitive neuroscience, etc.)

* Biological (growth of white shrimp)

* Ecological (analysis of pollutant concentration in urban areas and ecological status modelling of streams)

Recent Submissions

  • K nearest neighbour optimal selection in fuzzy inductive reasoning for smart grid applications 

    Jurado Gómez, Sergio; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco (Institute of Electrical and Electronics Engineers (IEEE), 2019)
    Conference report
    Open Access
    Energy recasting has been an area of great interest in the last years. It unlocks, not only the Smart Grid's potential with load balancing but also new business models and added value services. To achieve an accurate, ...
  • Charting perceptual spaces with fuzzy rules 

    Paz Ortiz, Alejandro Iván; Nebot Castells, M. Àngela; Romero Merino, Enrique; Múgica Álvarez, Francisco (Institute of Electrical and Electronics Engineers (IEEE), 2019)
    Conference lecture
    Open Access
    Algorithmic music nowadays performs domain specific tasks for which classical algorithms do not offer optimal solutions or require user's expertise. Among these tasks is the extraction of models from data that offer an ...
  • Energy performance forecasting of residential buildings using fuzzy approaches 

    Nebot Castells, M. Àngela; Múgica Álvarez, Francisco (2020-01-20)
    Article
    Open Access
    The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. ...
  • Comparison between composite index solution surfaces with fuzzy composite index decision surfaces 

    González Cárdenas, Rubén; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco (Institute of Electrical and Electronics Engineers (IEEE), 2019)
    Conference lecture
    Open Access
    Composite indices are used in many of the traditional approaches to measure risk to natural hazards. However, such indices are often built assuming linear interdependencies between the aggregated components, comprising in ...
  • Enhanced equal frequency partition method for the identification of a water demand system 

    Escobet Canal, Antoni; Huber Garrido, Rafael M.; Nebot Castells, M. Àngela; Cellier, François E. (Sarjoughian,H.S.; Cellier, F.E.; Marefat, M.M.; Rozenblit, J.W. (eds.) Institute of Electrical and Electronics Engineers, 2000)
    Conference report
    Open Access
    This paper deals with unsupervised partitioning. A first goal of this paper is to present an enhancement to the Equal Frequency Partition (EFP) method that allows to reduce, to some extent, the main drawback of this classical ...
  • The importance of interpretability and visualization in machine learning for applications in medicine and health care 

    Vellido Alcacena, Alfredo (2019-02-04)
    Article
    Open Access
    In a short period of time, many areas of science have made a sharp transition towards data-dependent methods. In some cases, this process has been enabled by simultaneous advances in data acquisition and the development ...
  • HIV drug resistance prediction with weighted categorical kernel functions 

    Ramon, Elies; Belanche Muñoz, Luis Antonio; Pérez Enciso, Miguel (2019-07-30)
    Article
    Open Access
    Background: Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug ...
  • Hybrid evolutionary data analysis technique for environmental modeling 

    Acosta, Jesus; Nebot Castells, M. Àngela; Fuertes Armengol, José Mª (International Centre for Numerical Methods in Engineering (CIMNE), 2006)
    Conference report
    Open Access
    In this work an evolutionary fuzzy system (EFS) is presented and applied to an environmental problem, i.e. modeling ozone concentrations. The hybrid system is composed by a FIR methodology and a genetic algorithm (GA) that ...
  • Wrapper-based fuzzy inductive reasoning model identification for imbalance data classification 

    Bagherpour, Solmaz; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco (Institute of Electrical and Electronics Engineers (IEEE), 2018)
    Conference lecture
    Open Access
    Fuzzy Inductive Reasoning (FIR) is a qualitative inductive modeling and simulation methodology for dealing with complex dynamical systems. FIR has proven to be a powerful tool for qualitative model identification and ...
  • Systematic analysis of primary sequence domain segments for the discrimination between class C GPCR subtypes 

    König, Caroline; Alquézar Mancho, René; Vellido Alcacena, Alfredo; Giraldo Arjonilla, Jesús (2018-03-01)
    Article
    Open Access
    G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate ...
  • Bridging deep and kernel methods 

    Belanche Muñoz, Luis Antonio; Ruiz Costa-Jussà, Marta (2017)
    Conference report
    Restricted access - publisher's policy
    There has been some exciting major progress in recent years in data analysis methods, including a variety of deep learning architectures, as well as further advances in kernel-based learning methods, which have demonstrated ...
  • A fuzzy rule model for high level musical features on automated composition systems 

    Paz Ortiz, Iván; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Romero Merino, Enrique (Springer, 2017)
    Part of book or chapter of book
    Open Access
    Algorithmic composition systems are now well-understood. However, when they are used for specific tasks like creating material for a part of a piece, it is common to prefer, from all of its possible outputs, those exhibiting ...

View more