Using sequential approximate optimization and a genetic algorithm to calibrate agent-based models

Roberto Borquez, Enrique Canessa, Carlos Barra, Sergio Chaigneau

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

We present a Genetic Algorithm (GA) tool that uses Sequential Approximate Optimization (SAO) to calibrate Agent-Based Models (ABMs). The SAO/GA searches through a user-defined set of input parameters to an ABM, delivering values for those parameters so that the output time series of an ABM match the real system's time series to certain precision. SAO/GA calculates a meta-model of the real and ABM's time series and optimizes that model. This allows SAO/GA to stabilize the ABMï¿1/2s time series and assure a higher probability of convergence, even under highly variable ABM's outputs. The results show that SAO/GA exhibits a higher convergence probability, but requires a rather long computational time to reach the stopping condition, although that long time is not so excessive to preclude SAO/GA practical use.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2015 34th International Conference of the Chilean Computer Science Society, SCCC 2015
EditorialIEEE Computer Society
ISBN (versión digital)9781467398176
DOI
EstadoPublicada - 23 feb. 2016
Publicado de forma externa
Evento34th International Conference of the Chilean Computer Science Society, SCCC 2015 - Santiago, Chile
Duración: 9 nov. 201513 nov. 2015

Serie de la publicación

NombreProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volumen2016-February
ISSN (versión impresa)1522-4902

Conferencia

Conferencia34th International Conference of the Chilean Computer Science Society, SCCC 2015
País/TerritorioChile
CiudadSantiago
Período9/11/1513/11/15

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