Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling

Title: Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling

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Abstract

Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.

Publication Date:: Tue May 01 00:00:00 EDT 2007

Research Org.:: Idaho National Lab. (INL), Idaho Falls, ID (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 915524

Report Number(s):: INL/CON-07-12404
TRN: US200817%%563

DOE Contract Number:: DE-AC07-99ID-13727

Resource Type:: Conference

Resource Relation:: Conference: 2007 IEEE Conference on Technologies for Homeland Security,Boston,05/16/2007,05/17/2007

Country of Publication:: United States

Language:: English

Subject:: 45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE; ALGORITHMS; ENGINES; GENETICS; IDAHO; IMPLEMENTATION; NATIONAL SECURITY; OPTIMIZATION; PLANNING; SECURITY; SIMULATION; TESTING

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                    . Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling.  United States: N. p., 2007. 
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                    . Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling.  United States.

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                    . 2007.  
        "Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling".  United States.  https://www.osti.gov/servlets/purl/915524.

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@article{osti_915524,

  title        = {Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling},

  author       = {},

  abstractNote = {Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/915524},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue May 01 00:00:00 EDT 2007},

  month        = {Tue May 01 00:00:00 EDT 2007}

}

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