The area of *evolving intelligent systems* (EIS) has matured over the last decade. The origins of the adaptive self-learning systems can be traced back to the works of Y. Tsypkin [1] and T. J. Proczyk with E. H. Mamdani [2] from 1970s. The pioneering works of Tsypkin, however, were limited to the conventional automatic control as opposed to the more generic intelligent and autonomous systems, which where developed later and are of primary interest in the EIS area now. Procyk and Madani proposed an original approach for fuzzy rule-based controller design, but it is closer to the adaptive system rather than to evolving. In the context of this article and increasingly more widely today the term ‘evolving’ is used in the sense of the self-development of a system **(in terms of both its structure and parameters) based on the stream of data** coming to the system on-line and in real-time from the dynamically changing environment and the system itself [3]. Evolving (intelligent systems) should be distinguished from adaptive systems (which are often linear and concern parameters of the system only) the theory of which was well developed from 1970s [4] and also from evolutionary systems which concern mathematical approaches borrowing from the natural (population) evolution and use such paradigms as crossover, mutation, selection, etc. [5].

Until the turn of the centuries there was no systematic approach which to point to the system structure identification (e.g. fuzzy rules, fuzzy sets, input variables in the case of fuzzy-rule-based systems; neurons in the case of neural network system; states in the hidden Markov models; decision rules in decision trees; words/vocabulary in fuzzy grammar, etc.). Around a decade ago the fundamentals of evolving intelligent systems theory were set up mainly in two independent books [6], [7], but also in a series of parallel articles [8]-[25] and activities (annual conferences, tutorials, workshops, summer schools, special issues, a new journal (Evolving Systems, ISSN 1868-6478), a new TC.

The area of EIS now includes sub-topics of;

- Evolving clustering
- Evolving classifiers
- Evolving Controllers
- Evolving Time-Series Predictors
- Evolving Hidden Markov Models
- Evolving Decision Trees
- Evolving Fuzzy Grammar
- Evolving Fuzzy Rule-based Systems
- Evolving Neural Networks
- Various applications...

In this special issue of the eNewsLetter of the Systems, Man and Cybernetics Society there are three papers which cover some of the above topics, namely

- ‘Evolving Clustering: An Asset for Evolving Systems’ by Dr. Abdelhamid Bouchachia;
- ‘Selected Topics of Evolving Intelligent Systems’ by Drs. Jose de Jesus Rubio, Manuel Jimenez, Humberto Perez, and Maricel Figueroa;
- ‘Human Inspired Evolving Machines – The Next Generation of Evolving Intelligent Systems’ by Dr. Edwin Lughofer.

Intelligent Systems’ by Dr. Edwin Lughofer.

No doubt that this paper will only be a small step in the direction of informing and exciting the colleagues researchers about the great achievements and opportunities of this newly emerging topic and more will follow. More details can also be fund at the web sites of;

- Technical Committee on Evolving Intelligent Systems,
- The next annual conference on EAIS -
- The journal Evolving Systems,
- Task Force AEFS,

http://www.ieeesmc.org/technicalcommittess/tc_evis.html

http://www.uc3m.es/portal/page/portal/congresos_jornadas/home_cfp_eais

http://www.springer.com/physics/complexity/journal/12530

http://www.fee.unicamp.br/IEEE_AFS/index.php?option=com_content&task=view&id=13&Itemid=27

[1] Y. Tsipkin, Adaptation and Learning in Automatic Systems, Academic Press Inc., Orlando, FL, USA, 1971.

[2] T. J. Procyk, E. H. Mamdani, A linguistic Self-organising process controller, Automatica, vol. 15(1), Jan. 1979, pp.15-30.

[3] P. Angelov, Evolving fuzzy systems. In: Encyclopedia of complexity and systems science, p. 3242-3255 (Ed. Meyers RA). Springer2009.

[4] T. Kailath, A. H. Sayed, B. Hassibi, Linear Estimation, Upper Saddle Rive, NJ, USA: Prentice Hall, 2000.

[5] Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs, Berlin, Germany: Springer Verlag, 1996.

[6] P. Angelov, Evolving rule-based models A tool for design of flexible adaptive systems, Physica-Verlag Heidelberg: Heidelberg, 2002.

[7] N. Kasabov, Evolving connectionist sytems Methods and applictions in bioinformatics, brian study and ontelligent machines, Springer-Verlag London: London, 2003.

[8] Fritzke B: Growing cell sructures-A self-organizing network for unsupervised and supervised learning. Neural Networks. 1994;7(9):1441-1460

[9] Fritzke B: A growing neural gas network learns topologies. In: Advances in Neural Information Processing Systems 7, p. 625-632 Eds. Tesauro G, Touretzky DS, Leen TK). MIT Press: Cambridge, 1995.

[10] P. Angelov, Buswell R: Evolving rule-based models: A tool for intelligent adaptation. In: Joint 9th Ifsa World Congress and 20th Nafips International Conference, Proceedings, Vols 1-5, p. 1062-1067 Eds. Smith MH, Gruver WA, Hall LO), 2001.

[11] N. Kasabov, Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning. IEEE Transactions on Systems Man and Cybernetics-Part B. 2001;31(6):902-918.

[12] D. Filev, T. Larson and L. Ma (2000) Intelligent Control for Automotive Manufacturing - Rule-based Guided Adaptation, In Proc. of the IEEE Conference on Industrial Electronics, IECON-2000, Nagoya, Japan, Oct. 2000, pp. 283 - 288

[13] P. Angelov, Buswell R: Identification of evolving fuzzy rule-based models. IEEE Transactions on Fuzzy Systems. 2002;10(5):667-677.

[14] N. Kasabov, Song Q: DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Transactions on Fuzzy Systems. 2002;10(2):144-154.

[15] S. Marsland, Shapiro J, Nehmzow U: A self-organising network that grows when required. Neural Networks. 2002;15:1041-1058.

[16] P. Angelov, D. Filev, Flexible models with evolving structure. In: IEEE Symposium on Intelligent Systems, p. 28-33: Varna, Bulgaria, 2002.

[17] P. Angelov, D. Filev, On-line design of Takagi-Sugeno models. In: Lecture Notes in Computer Science 55, 2715 , Fuzzy Sets and Systems IFSA 2003, p. 576-584 Eds. Bilgic T, Baets BD, Kaynak O)2003.

[18] P. P. Angelov , D. P. Filev, An approach to online identification of Takagi-Sugeno fuzzy models, IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics. 2004;34(1):484-498.

[19] P. Angelov, C. Xydeas, D. Filev, On-line identification of MIMO evolving Takagi-Sugeno fuzzy models. In: IJCNN-FUZZ-IEEE, p. 55-60: Budapest, Hungary, 2004.

[20] P. Angelov, X. Zhou, Evolving fuzzy-rule-based classifiers from data streams. IEEE Transactions on Fuzzy Systems. 2008;16(6):1462-1475.

[21] P. Angelov, D.P. Filev, Simpl_eTS: A simplified method for lerning evolving Takagi-Sugeno fuzzy models. In: The 2005 IEEE International Confrence on Fuzzy Systems, p. 1068-1073: Reno, NE, USA, 2005.

[22] P. Angelov, X. Zhou, Evolving fuzzy systems from data streams in real-time. In: 2006 International Symposium on Evolving Fuzzy Systems, p. 29-35 2006.

[23] M. Futschik, A. Reeve, N. Kasabov, Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissues. Artif Intell Med. 2003;28:165-189.

[24] X. Zhou, P. Angelov, Autonomous visual self-localization in completely unknown environment using evolving fuzzy rule-based calssifier. In: IEEE Symposium on Computational Intelligence in Security and Defense Applications, p. 131-138 2007.

[25] Leng G, McGinnity TM, Prasad G: An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets and Systems. 2005;150:211-243.

[26] Rong H-J, Sundararajan N, Huang G-B, Saratchandran P: Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets and Systems. 2006;157:1260-1275.

[27] E. D. Lughofer, FLEXFIS: A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems. 2008;16(6):1393-1410.

[28] R. Ballini, A.R.R. Mendonca, F. Gomide, Evolving fuzzy modeling in risk analysis. Intelligent Systems in Accounting, Finance and Management. 2009;16:71-86.

[29] J. Kelly, P. Angelov, M. J. Walsh, H. M. Pollock, M.A. Pitt, et al.: A self-learning fuzzy classifier with feature selection for intelligent interrogation of mid-IR spectroscopy data derived from different categories of exfoliative cervical cytology. International Journal on Computational Intelligence Research , special issue on the future of fuzzy systems research. 2008;4(4):392-401.

[30] P. Angelov, A. Kordon, Adaptive inferential sensors based on evolving fuzzy models: an industrial case study. IEEE Transactions on Systems, Man, and Cybernetics –B. 2010;40 (2):529-539.

[31] P. Angelov, R. Yager, A simple rule-based system through vector membership and kernel-based granulation. In: 5th International Conference on Intelligent Systems, IS-2010, p. 349-354. IEEE Press: London, England, UK, 2010.

[32] P. Angelov, Evolving Takagi-Sugeno fuzzy systems from streaming data (eTS+). In: Evolving Intelligent Systems, p. 21-50 Eds. Angelov P, Filev D, Kasabov N). John Wiley & Sons: Hoboken, New Jersy, 2010.

[33] J. A. Iglesias, P. Angelov, A. Ledezma, A. Sanchis, Evolving classification of agents’ behaviors: a general approach. Evolving Systems 2010;1(3):161-171.

[34] E. Lughofer, On-line evolving image classifiers and their appliction to surface inspection. Image and Vision Computing. 2010;28(7):1065-1079.

[35] D. Coyle, G. Prasad, T.M. McGinnity, On utilizing self-organizing fuzzy neural networks for financial forecasts in the NN5 forecasting competition. In: International Joint Conference on Neural Networks (IJCNN), p.1-8 IEEE: Barcelona, 2010.

Plamen Angelov, Guest Editor

Lancaster, England