Evolving Intelligent Systems: Methods, Learning, & Applications

The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of Evolving Intelligent Systems are summarized 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 human knowledge 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 progress in Genetic Algorithms, Evolutionary Computing, and Genetic Programming. 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 attention. These systems called `evolving' came as a result of the research on practical intelligent systems and on-line learning algorithms that are capable of extracting knowledge from data and performing a higher level adaptation of model structure as well as model parameters. Evolving systems can also be considered an extension of the multi-model concept known from the control theory, and of the on-line identification of fuzzy rule-based models. They can also be regarded as an extension of the methods for on-line learning neural networks with flexible structure that can grow and shrink.