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DANIEL TAURITZ
D. Tauritz


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

Computational Intelligence with emphasis on Meta-Heuristic Techniques inspired by nature such as Evolutionary Algorithms with applications in Cyber Security, 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 losing 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

Matthew A. Martin and Daniel R. Taurit, “A Problem Configuration Study of the Robustness of a Black-Box Search Algorithm Hyper-Heuristic”, In Proceedings of the 16th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '14), Vancouver, BC, Canada, July 12-16, 2014.

Nathaniel R. Kamrath and Brian W. Goldman and Daniel R. Taurit, “Using Supportive Coevolution to Evolve Self-Configuring Crossover”, In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO ‘13), pages 1489-1496, Amsterdam, The Netherlands, July 6-10, 2013.

Matthew A. Martin and Daniel R. Taurit, “Evolving Black-Box Search Algorithms Employing Genetic Programming”, In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO ‘13), pages 1497-1504, Amsterdam, The Netherlands, July 6-10, 2013.

Brian W. Goldman and Daniel R. Taurit, “Linkage Tree Genetic Algorithms: Variants and Analysis”, In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO ‘12), pages 625-632, Philadelphia, U.S.A., July 7-11, 2012.

Josh L. Wilkerson, Daniel R. Taurit, and James Bridges, “Multi-Objective Coevolutionary Automated Software Correction System”, In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO ‘12), pages 1229-1236, Philadelphia, U.S.A., July 7-11, 2012.

Brian W. Goldman and Daniel R. Taurit, “Supportive Coevolution”, In Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO ‘12), pages 59-66, Philadelphia, U.S.A., July 7-11, 2012.

Brian W. Goldman and Daniel R. Taurit, “Self-Configuring Crossover”, In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO ‘11), pages 575-582, Dublin, Ireland, July 12-16, 2011.

Recent Grants

Los Alamos National Laboratory (LANL), “Cyber Security Sciences Institute”, (PI, 100% credit), $500,000, 10/1/2013-9/30/2018.

Sandia National Laboratories (SNL), “Computational Intelligence Techniques for Situational Awareness in Computing Networks”, (PI, 100% credit), $299,680, 11/4/2011-9/30/2014.

Los Alamos National Laboratory (LANL), “Cyber Security Sciences Institute”, (PI, 100% credit), $78,034, 3/7/2013-9/30/2013.

National Security Agency (NSA), “Common Correctness for Protecting Confidentiality of Critical Infrastructure Systems”, (Co-PI, 10% shared credit), $41,250, 8/31/2011-11/30/2012.

University of La Verne, “Computational Intelligence Techniques for Virtual Facilitation”, (PI, 100% credit), $57,066, 6/1/2011-8/15/2012.

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