Red Teaming with Coevolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Hingston:2011:RTwC,

• title = "Red Teaming with Coevolution",
• author = "Philip Hingston and Mike Preuss",
• pages = "1155--1163",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Coevolutionary systems, Evolutionary simulation-based optimization, Real-world applications",
• DOI = "doi:10.1109/CEC.2011.5949747",
• abstract = "In this paper we present a coevolutionary algorithm designed to be used as a computational tool to assist in red teaming studies. In these applications, analysts seek to understand the strategic and tactical options available to each side in a conflict situation. Combining scenario simulations with a coevolutionary search of parameter space is an approach that has many attractions. We argue that red teaming applications are sufficiently different from many others where coevolution is used so that specially designed algorithms can bring advantages. We illustrate by presenting a new algorithm that simultaneously evolves strong strategies along with dangerous counter-strategies. We test the new algorithm on two example problems: an abstract problem with some difficult characteristics; and a practical red teaming scenario. Experiments show that the new algorithm is able to solve the abstract problem well, and that it is able to provide useful insights on the red teaming scenario.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for Philip Hingston Mike Preuss

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