\section{Introduction}\label{intro}\vspace{1ex}%\noindent

Control  engineering often must consider the control of poorly
understood systems such as chemical, industrial or economic
processes for the benefit of society.  The present challenge to
control engineers is the modeling and control of complex,
interrelated systems under uncertainty such as  traffic-control
systems, robotic systems, and industrial automation systems. The
necessity to maintain high system performance under uncertain and
contingent changes in the model of operating conditions, has led
to development of great many numerical methods, now often called
``classical.'' They provide the solid basis for change point
detection, for change isolation, for after-change model
identification, and lastly, for system accommodation (adaptation)
to the newly emerged operating conditions \cite{blanke:1999}.

Implementation  of the classical methods is generally an intricate
problem. The classical numerical and mathematical programming
methods are not able sometimes to guarantee good results: reliable
detection and quick-acting adaptation for low computational cost.
The fact that sometimes they cannot provide the satisfactory
results in terms of benefit-cost ratio or performance-cost ratio
or quality-cost ratio has heightened the need for borrowing the
adaptation mechanisms from the natural evolution.

Evolutionary  algorithms are relatively new, but very powerful
techniques intended to find solutions to many real-world search
and optimization problems \cite{deb:2001}. Evolutionary
Computing and other non-standard approaches such as Fuzzy
Technology and Neural Networks, are merged in the area of
``Intelligent Techniques'' or ``Computational Intelligence'' (CI).
The last three decades have witnessed a very strong growth of CI.

The genetic  methods within CI are of special interest because of
their potential ability to handle complex optimization problems
using a simple and relatively inexpensive model. Motivated by
genetics, the basic idea of GAs is to select the best performer
(in our case, the best filter) from a population and thus to
improve control from trial to trial. Such remedial actions can be
treated as system iterated design optimization, or system
re-design over life-cycle years.

In this   paper, we solve the problem of filter optimization for
stochastic control under uncertainties in a class of systems known
as trackers \cite{maybeck:1982}. Two approaches to filter
optimization are experimentally compared: the classical one and
that based on GAs.

The  outline of the paper is as follows. Section~\ref{problem}
describes the problem including the monitored system, Kalman
filter and adaptive filter.  A system example and a brief
description of applied genetic algorithms are given in
Section~\ref{genetic}. Some computational results are presented
and discussed in Section~\ref{experiment}. Finally,
Section~\ref{conclusions} concludes the paper.
