Applying 3D printing and genetic algorithm-generated anticipatory system dynamics models to a homeland security challenge

Created by W.Langdon from gp-bibliography.bib Revision:1.8178

@InProceedings{North:2015:WSC,

• author = "Michael J. North and Pam Sydelko and Ignacio Martinez-Moyano",
• booktitle = "2015 Winter Simulation Conference (WSC)",
• title = "Applying {3D} printing and genetic algorithm-generated anticipatory system dynamics models to a homeland security challenge",
• year = "2015",
• pages = "2511--2522",
• month = dec,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7408361",
• DOI = "doi:10.1109/WSC.2015.7408361",
• abstract = "In this paper we apply 3D printing and genetic algorithm-generated anticipatory system dynamics models to a homeland security challenge, namely understanding the interface between transnational organized criminal networks and local gangs. We apply 3D printing to visualize the complex criminal networks involved. This allows better communication of the network structures and clearer understanding of possible interventions. We are applying genetic programming to automatically generate anticipatory system dynamics models. This will allow both the structure and the parameters of system dynamics models to evolve. This paper reports the status of work in progress. This paper builds on previous work that introduced the use of genetic programs to automatically generate system dynamics models. This paper's contributions are that it introduces the use of 3D printing techniques to visualize complex networks and that it presents in more detail our emerging approach to automatically generating anticipatory system dynamics in weakly constrained, data-sparse domains.",
• notes = "Also known as \cite{7408361}",

}

Genetic Programming entries for Michael J North Pam Sydelko Ignacio Martinez-Moyano

Citations