dr. Kenneth DEJONG
Professor of Computer Science
George Mason University
S.U.A.
-----------------------------------
          
Understanding Complex Adaptive Systems: An Evolutionary Agent-based
Approach
-----------------------------------
          
ABSTRACT
-----------------------------------
The world is full of complex adaptive systems, such as computer
networks,
stock markets, biological systems, and economies. There continues
to be a
need to understand the underlying dynamics of these systems
in order to
better design, predict, respond to, and modify them. However,
because they invariably consist of a large number of components
that interact in
non-linear ways, they are extremely difficult for the human
mind to grasp.
Furthermore, they generally defy formal mathematical analysis
without
making unrealistic assumptions regarding linearity and independence.
A promising alternative approach is the use of modeling and
simulation to
augment the human cognitive system. To be effective, the modeling
and
simulation tools brought to bear must be both effective in
capturing the
non-linear dynamics and scalable to .real-world. problems.
We believe that
this is now possible via the synergistic blending of two mature
technologies: agent-based modeling and evolutionary computation.
The
agent-based modeling technology allows one to effectively model
the
non-linear dynamics as an emergent property of the interactions
among
agents. The evolutionary computation technology endows these
agents with the ability to adapt their behavior over time, creating
a co-evolutionary dynamic capable of generating important insights
into realistic, yet previously unencountered scenarios.
This talk will describe the tools and techniques we have developed
for this purpose, and illustrate their application to computer
network security and treatments for inhalation anthrax.
          
SHORT CV
-----------------------------------
Kenneth A. De Jong received his Ph.D. in computer science from
the
University of Michigan in 1975. He joined George Mason University
in 1984 and is currently a Professor of Computer Science, head
of the Evolutionary Computation Laboratory, and associate director
of the Krasnow Institute. His research interests include genetic
algorithms, evolutionary computation, machine learning, and
adaptive systems. He is currently involved in research projects
involving the development of new evolutionary algorithm (EA)
theory, the use of EAs as heuristics for NP-hard problems, and
the application of EAs to the problem of learning task programs
in domains such as robotics, diagnostics, navigation and game
playing. He is also interested in experience-based learning
in which systems must improve their performance while actually
performing the desired tasks in environments not directly their
control or the control of a benevolent teacher. Support for
these projects is provided by DARPA, ONR, and NRL. He is an
active member of the Evolutionary Computation research community
and has been involved in organizing many of the workshops and
conferences in this area. He is the founding editor-in-chief
of the journal Evolutionary Computation (MIT Press), and a member
of the board of ACM SIGEVO.