The term Evolving was first used to describe an intelligent system in 1996 by B. Carse, T. Fogarty and A Munro for a fuzzy rule-based controller where its parameters and structure were learnt simultaneously using a Genetic Algorithm. Years later, alternative methods to learn an evolving intelligent system (EIS) via Incremental learning were suggested as a neuro-fuzzy algorithm by N. Kasabov in 1998 and a rule-based model by P. Angelov in 1999.
EIS are usually associated with, streaming data and on-line (often real-time) modes of operation. They can be seen as adaptive intelligent systems. EIS assumes on-line adaptation of system structure in addition to the parameter adaptation which is usually associated with the term "incremental" from Incremental learning. They have been studied as a methodological solution to learn from streaming data exhibiting non-stationary behaviours by M. Sayed-Mouchaweh and E. Lughofer.
An important sub-area of EIS is represented by Evolving Fuzzy Systems (EFS) (a comprehensive survey written by E. Lughofer including real-world applications can be found in ), which rely on fuzzy systems architecture and incrementally update, evolve and prune fuzzy sets and fuzzy rules on demand and on-the-fly. One of the major strengths of EFS, compared to other forms of evolving system models, is that they are able to support some sort of interpretability and understandability for experts and users. This opens possibilities for enriched human-machine interaction's scenarios, where the users may "communicate" with an on-line evolving system in form of knowledge exchange (active learning (machine learning) and teaching). This concept is currently motivated and discussed in the evolving systems community under the term Human-Inspired Evolving Machines and respected as "one future" generation of "EIS".