Understanding Complex Adaptive Systems: An Evolutionary Agent-based Approach

Dr. Kenneth De Jong
Thursday, March 30, 2006
3:00PM ~ 4:30PM - CSB 232

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 Bio


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 the 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 the ACM SIGEVO.