Soft Computing is a field of artificial intelligence that exploits the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low computational cost when solving real-world problems in a huge amount of application fields. SC focuses on the design of hybrid intelligent systems that combine nature-inspired computational approaches to adequately handle uncertain information, vague and incomplete data, and to develop optimization, machine learning and knowledge discovery. Among those methods, fuzzy logic extends classical logic to provide a conceptual framework for reasoning and knowledge representation under imprecision and uncertainty based on fuzzy sets theory and implemented through fuzzy systems. Evolutionary computation provides single and multi-objective computational models for optimization, search and machine learning purposes that have their origin in evolution theories and Darwinian natural selection. Genetic algorithms are probably the most representative evolutionary algorithm. Some other general purpose methods from the extensive family of metaheuristic algorithms are also considered to perform robust search in complex spaces obtaining high quality solutions in reasonable time.

The SOCCER lab is devoted to develop advanced research in theoretical aspects and practical applications of soft computing. Its members are especially focused on solving complex real-world problems using soft computing methods. We have a deep previous experience on providing soft computing solutions to many different application areas as heating ventilation-air conditioning system control, information retrieval, electrical energy distribution, assembly line balancing, visual science map design and mining, medical image segmentation and registration, skeleton-based forensic identification, and economic modeling.