Evolving Intelligent Systems
----------------------------

One of the important research challenges 
today is to develop new theoretical methods, 
algorithms, and implementations of systems 
with a higher level of flexibility and autonomy, 
we can say with higher level of intelligence. 
These systems have to be able to evolve their 
structure and knowledge on the environment and 
ultimately – evolve their intelligence. To address 
the problems of modelling, control, prediction, 
classification and data processing in a dynamically 
changing and evolving environment, a system must 
be able to fully adapt its structure and adjust its 
parameters, rather than use a pre-trained and a fixed 
structure. That is, the system must be able to evolve, 
to self-develop, to self-organize, to self-evaluate 
and to self-improve.
 
The newly established concept of evolving intelligent 
systems (eIS) is a result of the 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. To address the problems of modelling, 
control, prediction, classification and data processing 
in a dynamically changing and evolving environment, a system 
must be able to fully adapt its structure and adjust its 
parameters, rather than use a pre-trained and a fixed structure. 
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 eIS as a realistic and practical 
tool for design of real time intelligent systems.

The one week course (which will comprise three two-hour 
lectures) will introduce the postgraduate students of the 
leading Spanish and European University of Carlos III, 
Madrid into;
    a)principles and methodology of evolving intelligent 
systems in the context of machine learning, mining data 
streams and system identification;
    b)algorithms and software concepts and examples from 
the area of eIS;
    c)applications to various industrial, defence and research 
projects in which the author took an active part.