Associate Professor
Ph.D., Leiden University , 2002
M.S., Leiden University , 1996
B.S., Leiden University , 1992
Personal Web-Site: http://web.mst.edu/~tauritzd/

Areas of Interest

Evolutionary Algorithms, Natural Computation, Artificial Intelligence, Combinatorial Optimization Problems in Critical Infrastructure Protection, and Automated Software Engineering.

Modern society is faced with ever more complex problems that have so many potential solutions that even all the computers on the planet put together cannot exhaustively try them all to determine the best solution. Think of, for example, developing new cancer medications, modeling the stock market, designing super efficient circuit diagrams, identifying the most critical threats to our critical infrastructures and corresponding defenses, and many more! These problems have in common the extremely large number of potential solutions as well as that we do not need necessarily the theoretically best solution, but would be satisfied with a `good enough’ solution. Heuristic search algorithms are a type of algorithm which employs ‘rules of thumb’ to efficiently search for a `good enough’ solution; they obtain their efficiency at the expense of loosing any guarantee of finding the theoretical best solution. My favorite type of heuristic search algorithm is the Evolutionary Algorithm (EA), because it performs well over a wide variety of really hard types of problems. EAs are stochastic, population-based heuristic search algorithms inspired by neo-Darwinian evolution theory and Mendelian genetics.

Selected Publications

T. C. Service and D. R. Tauritz. Increasing Infrastructure Resilience Through Competitive Coevolution. New Mathematics and Natural Computation, 5(2):441-457, 2009.

T. C. Service and D. R. Tauritz. Free Lunches in Pareto Coevolution. In Proceedings of GECCO 2009 - the Genetic and Evolutionary Computation Conference, Montreal, Canada, July 8-12, 2009.Nominated for best theory track paper award.

J. Leopold and D. R. Tauritz. An Interactive Student-Driven Program to Facilitate Scholastic Achievement in Computer Science, Engineering, and Mathematics. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition, Austin, Texas, U.S.A., June 14-17, 2009.

W. M. Siever, D. R. Tauritz, A. Miller, M. L. Crow, B. McMillin, and S. Atcitty. Symbolic Reduction for High-Speed Power System Simulation. Simulation: Transactions of the Society for Modeling and Simulation International, 84(6):297-309, June 2008.

T. C. Service and D. R. Tauritz. A No-Free-Lunch Framework for Coevolution. In Proceedings of GECCO 2008 - the Genetic and Evolutionary Computation Conference, Atlanta, Georgia, U.S.A., July 12-16, 2008.

E. A. Smorodkina and D. R. Tauritz. Greedy Population Sizing for Evolutionary Algorithms. In Proceedings of CEC 2007 - IEEE Congress on Evolutionary Computation, pages 2181–2187, Singapore, September 25-28, 2007.

M. Johnson, D. R. Tauritz, R. Wilkerson. SNDL-MOEA: Stored Non-Domination Level MOEA. In Proceedings of GECCO 2007 - the Genetic and Evolutionary Computation Conference, pages 837–844, London, UK, July 7-11, 2007.  Nominated for best multi-objective optimization track paper award.

Recent Grants

A GPU-based High Performance Computing Cluster for Multiple Military Modeling Capabilities,  Department of Defense - Air Force Office of Scientific Research, $150K, 2009-2010, Co-PI.

Common Correctness for Protecting Confidentiality of Critical Infrastructure Systems, National Security Agency, $28K, 2009-2010, Co-PI.

Computer Science Recruitment for the 21st Century: Phase III, Computer Research Association's Committee on the Status of Women in Computing Research, $9.5K, 2008-2009, PI.

A Program to Facilitate Scholastic Achievement in Computer Science, Engineering, and Mathematics, National Science Foundation, $225K, 2004-2009, Co-PI.