Part of IEEE Symposium Series on Computational Intelligence 2011

The true intelligent systems should be dynamically evolving and be able to adapt and learn. The concept of evolving intelligent systems was established recently as a synergy between conventional systems, neural networks and fuzzy systems as structures for information representation and real time methods for machine learning. This emerging area targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. One of the important research challenges today is to develop methodologies, concepts, algorithms and techniques towards the design of intelligent systems with a higher level of flexibility and autonomy, so that the systems can evolve their structure and knowledge of the environment and ultimately - evolve their intelligence. That is, the system must be able to evolve, to self-develop, to self-organize, to self-evaluate and to self-improve. Wireless sensor networks, assisted ambient intelligence, embedded soft computing diagnostics and prognostics algorithms, intelligent agents, smart evolving sensors; autonomous robotic systems etc. are some of the natural implementation areas of evolving and adaptive intelligent systems. EAIS'11 continues the tradition set by the previous forums (EFS'06, GEFS'08, ESDIS'09, EIS'10) and is supported and organised by the Adaptive and Evolving Fuzzy Systems (AEFS) Task Force, FSTC, CIS, IEEE.


Topics

* New Adaptive and Evolving Learning Methods
o Stability, Robustness, Unlearning Effects
o Structure Flexibility and Robustness in Evolving Systems
o Evolving in Dynamic Environments
o Drift and Shift in Data Streams
o Self-monitoring Evolving Systems
o Evolving Decision Systems
o Evolving Perceptions
o Self-organising Systems
o Neural Networks with Evolving Structure
o Non-stationary Time Series Prediction with Evolving Systems
o Automatic Novelty Detection in Evolving Systems
o On-Line Identification of Fuzzy Systems
o Evolving Neuro-fuzzy Systems
o Evolving Fuzzy Clustering Methods
o Evolving Fuzzy Rule-based Classifiers
o Evolving Regression-based Classifiers
o Evolving Intelligent Systems for Time Series Prediction
o Evolving Intelligent System State Monitoring and Prognostics
* Methods
o Evolving Intelligent Controllers
o Evolving Fuzzy Decision Support Systems
o Evolving Consumer Behaviour Models
* Real-world application
o Robotics
o Control Systems
o Industrial Applications
o Data Mining and Knowledge Discovery
o Intelligent Transport
o Bio-Informatics
o Defence


Symposium Co-Chairs

Plamen Angelov, Lancaster University, UK
Dimitar Filev, Ford, USA
Nikola Kasabov, Aukland University of Technology, New Zealand


Program Committee

Adel Alimi
Plamen Angelov (Chair)
Jose Rubio Avila
Richard Duro
Panagiotis Chountas
Damien Coyle
Dimitar Filev (co-Chair)
Mario Gongora
Fernando Gomide
Antonio Medina Hernandez
Jose Iglesias
Janusz Kacprzyk
Petr Kadlec
Ilhem Kallel
Nik Kasabov (co-Chair)
Vitaliy Kolodyazhniy
Andre Lemos
Zsofia Lendek
Edwin Lughofer
Witold Pedrycz
Fernando Pouzols
Ignacio Rojas
Joao Sousa
Gancho Vachkov
Ronald Yager
Xiaojun Zeng