Ken De Jong
Professor of Computer Science and the Associate Director of the
Krasnow Institute at George Mason University
Friday September 23, 2005
2015 JKB, 12:00 PM
Evolutionary Computation: A Unified Approach
The field of Evolutionary Computation has experienced tremendous growth over the past 15 years, resulting in a wide
variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the
sense that, with such a wide variety of algorithms and approaches, it is often hard to see the relationships between them,
assess strengths and weaknesses, and make good choices for new application areas. This presentation is intended to give
an overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and
facilitates intelligent design choices. The use of this framework is then illustrated by showing how traditional EAs can be
compared and contrasted with it, and how new EAs can be effectively designed using it. Finally, the framework is used to
identify some important open issues that need further research.
Biography
Kenneth A. De Jong is Professor of Computer Science and the Associate Director of the Krasnow Institute at George Mason
University. Dr. De Jong's research interests include evolutionary computation, adaptive systems, and machine learning. He
is an active member of the evolutionary computation research community with a large number of papers, Ph.D. students,
and presentations in this area. He is also involved in the organization of many of the workshops and conferences on
evolutionary computation, and the founding Editor-in-chief of the journal Evolutionary Computation, published by MIT Press.
He is currently serving on the executive council of the International Society for Genetic and Evolutionary Computation.
Dr. De Jong is head of the Evolutionary Computation Laboratory at GMU, consisting of a group faculty members and
graduate students working on a variety of research projects involving the application of evolutionary algorithms to difficult
computational problems such as visual scene analysis and programming complex robot behaviors. This group is also
involved in extending current evolutionary computation models to include more complex mechanisms such as speciation,
co-evolution, and spatial extent. These ideas are being developed to improve both the applicability and scalability of current
evolutionary algorithms to more complex problem domains. Funding for the lab comes from a variety of sources including
DARPA, ONR, NRL, NSF, and local area companies. Further details can are available at www.cs.gmu.edu/~eclab.
Donuts will be provided