Evolving Intelligent Systems, eIS

The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of research that is on the crossroads of computational intelligence and cybernetics is compressed in this short communication. Intelligent systems can be defined as systems that incorporate some form of reasoning that is typical for humans. Fuzzy Systems are well known for being able to formalize the approximate reasoning that still separates humans from machines. Artificial neural networks have proven to be a useful form of parallel processing of information that employs principles from the organization of the brain. Finally, the evolution is a phenomenon that was initially used to solve optimization problems inspired by the so called 'genetic algorithms' due to D. E. Goldberg and 'genetic programming' due to J. Koza. These types of evolutionary algorithms are mimicking the natural selection that takes place in populations of living creatures over generations. More recently, the evolution of individual systems within their life-span (self-organization, learning through experience, and self-developing) has attracted the attention. These systems called 'evolving' came as a result of the research into the development of practical on-line algorithms that work in real-time and are close to the theoretically optimal, analytical solutions, suitable for non-stationary, non-linear problems of modeling, control, prediction, classification, clustering, signal processing. Due to the limited space and the specific purpose of this communication only the basic elements of the concept will be outlined. This concept represents, in fact, a higher level adaptation that concerns model structure as well as model parameters. It can also be considered as an extension of the multi-model concept known from the control theory, and of the on-line identification of fixed structure fuzzy rule-based models. It can also be considered as an extension of the learning neural networks methods in direction of on-line applications with a structure that can grow and shrink. This new concept of 'evolving intelligent systems' can also be treated in the framework of the knowledge and data integration. Evolutionary, population/generation based computation, can be applied to optimize parameters and features of an individual system, that learns incrementally from incoming data. The specific of this paper lays in the generalization of the recent advances in the development of evolving fuzzy and neuro-fuzzy models and the more analytical angle of consideration through the prism of knowledge evolution as opposed to the usually used data-centred approach. This powerful new concept has been recently introduced by the authors in a series of parallel works and is still under intensive development. It forms the conceptual basis for the development of the truly intelligent systems. A number of applications of this technique to a range of industrial and benchmark processes have been recently reported. Due to the lack of space only some of them will be mentioned primarily with illustrative purpose