A key characteristic of intelligent systems is their ability to deduce new knowledge, to predict and make decisions. The theoretical underpinning of these capabilities mainly resides in approximate reasoning, which itself is based on fuzzy logic and fuzzy sets. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans. The main challenge today is to design the next generation of intelligent systems able to have a higher level of flexibility and autonomy that can develop their understanding of the environment and ultimately their intelligence. It is to be noticed that the environments in which such systems are required to successfully operate are very often challenging – they are non-stationary, (often unpredictably) changing, and partially or completely unknown. To address the problems of modeling, control, prediction, classification and data processing in such environments a system model must be able to fully adapt, not simply to adjust parameters of a pre-trained and fixed structure. That is, the system model must be able to evolve, to self-develop, to self-organize. A new area that is addressing these challenges is now emerging on the cross-roads of reasoning-based fuzzy systems and evolution-inspired principles of adaptation, life-long learning, self-development and self-organization. It aims to develop systems that are more flexible than conventional adaptive systems that usually assume linearity and fixed structures of the underlying models. The emerging area of evolving fuzzy systems targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. Evolving fuzzy systems are evolution-inspired. They focus on the evolution of individual fuzzy systems. They use inheritance and gradual change with the aim of life-long learning and adaptation, as well as self-organization (including system structure evolution) in order to adapt to the (unknown and unpredictable) environment. This complements well with the established area of genetic fuzzy systems (GFS), that uses techniques based on population-based evolutionary and bio-inspired algorithms (genetic algorithms, genetic programming, evolution strategies, particle swarm optimization methods, bio-memetic approaches, ant colony optimization, etc.) to design fuzzy systems. These two topics make the methodological body of the present Symposium, which is the second in the series of events organized by the GFS Task Force from the Fuzzy Systems Technical Committee, IEEE Computational Intelligence Society. The present Symposium is organized by the Department of Communication Systems, Infolab21, Faculty of Science and Technology, Lancaster University, and technically co-sponsored by the Computational Intelligence Society and Systems, Man, and Cybernetics Society, IEEE, by the International Fuzzy Systems Association (IFSA), and by the European Society on Fuzzy Logic and Technology (EUSFLAT). The Symposium was also generously sponsored by EPSRC-UK arranging a number of student and young researchers’ grants that made possible attendance for a number of talented young people, and co-sponsored by Nokia-UK, BAE Systems, Retail Analytics, and J&S Marine offering a range of ‘best paper’ awards. This volume collects 55 full papers by 122 authors from 23 countries from all continents that have been accepted after being evaluated by at least three independent referees from the International Program Committee. All together, 72 papers authored by 153 authors from 26 countries were submitted and all of them were subjected to anonymous peer review process. The rejection rate of 24% (due to quality or irrelevance) is one of the illustrations of the high quality. The overall number of authors who have contributed to the symposium is another illustration of the International status of the event. The presence of several IEEE Fellows and a number of Senior Members and IEEE Technical Committees members is another fact in support of this qualification. Bearing in mind that this is only the second event of this series, the significant industrial participation (General Electric, USA, Ford Motor Co., USA, Nokia-UK, CEPSA, Spain etc.) illustrates the applicability of the methodologies that are discussed at the Symposium. The structure of the Symposium is composed of two main parts and eleven sessions: i) methodology-based sessions (covering evolving Takagi-Sugeno fuzzy models, evolving neuro-fuzzy systems, evolving fuzzy clustering, evolutionary fuzzy systems, genetic fuzzy systems); and ii) applications-oriented sessions (including industrial applications of real-time evolving fuzzy systems, evolving neuro-fuzzy systems, genetic fuzzy systems, developments in fuzzy systems and a session comprising papers that will discuss and explore the frontiers of computational intelligence and will pose some challenging questions. The discussion will take place in addition to scheduled sessions and networking events during the Round Table. (c) IEEE Press