Computing That Serves

Evolutionary Computation: A Unified Approach


Friday, September 23, 2005 - 11:00am


Ken De Jong, Professor of Computer Science and Associate Director of the Krasnow Institute at George Mason University

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.


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