Advances in artificial neural networks and machine learning

This work aims at a reflection on the evolution of the field of Neurocomputing along the last 20 years that have witnessed the sequence of editions of the International Work-Conference on Artificial Neural Networks (IWANN). This reflection arises inextricably of the evolution of connectionist networks themselves, describing their features and most remarkable particularities, most of which have prevailed in time.Another trend that is worth mentioning is the development of a strong interconnection with other paradigms comprised under the so-called Computational Intelligence, which can be understood as a set of nature-inspired computational methodologies and approaches to address complex real-world problems, which traditional approaches are ineffective or unfeasible to deal with. Indeed, many hybrid computational intelligence schemes have been developed that efficiently combine procedures from the domains of artificial neural networks, machine learning, evolutionary computation and fuzzy logic to be applied in complex domains.Finally, a brief description of the diverse contributions that have been included in this special issue is presented. These papers stem from previous versions presented at IWANN2011.