**About the Editors**

**Francisco A. G ´omez Vela** received his Ph.D. in Computer Science from the Pablo de Olavide University of Seville, in addition to Computer Science Engineering from the University of Seville. His lines of research are focused on the treatment of information using intelligent techniques, applying machine learning and data mining techniques. He has focused mainly on the analysis of genetic and biomedical data in his research. His research is mainly based on the inference of biological models based on gene networks. In addition, he has recently focused on the research of new big data techniques for the exploitation of different types of data.

**Federico Divina** obtained his Ph.D. in Artificial Intelligence from the Vrije Universiteit of Amsterdam, and, after that, worked as a postdoc at the University of Tilburg, within the European project NEWTIES. In 2006, he moved to the Pablo de Olavide University. He has been working on knowledge extraction since his Ph.D. thesis at the Vrije Universiteit of Amsterdam. His main research interests focus on machine learning, and in particular on techniques based on soft computing, bioinformatics, and big data.

**Miguel Garc´ıa-Torres** is an Associate Professor at the Escuela Politecnica Superior of the ´ Universidad Pablo de Olavide. He received his BS degree in physics and Ph.D. degree in computer science from the Universidad de La Laguna, Tenerife, Spain, in 2001 and 2007, respectively. After obtaining the doctorate he held a postdoc position in the Laboratory for Space Astrophysics and Theoretical Physics at the National Institute of Aerospace Technology (INTA). There, he joined the Gaia mission from the European Space Agency (ESA) and started to participate in the Gaia Data Processing and Analysis Consortium (DPAC) as a member of "Astrophysical Parameters", Coordination Unit (CU8). He has been involved in the "Object Clustering Analysis" (OCA) Development Unit since then. His research areas of interest include machine learning, metaheuristics, big data, time series forecasting, bioinformatics, and astrostatistics.
