Paper Info
Title
Calibrating Agent-Based Models Using an Improved Genetic Algorithm
Abstract
We present an improved GA-based tool that calibrates Agent-Based Models (ABMs). The 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. The improvements are focused on shortening the computational time that the GA needs to find good solutions. Additionally, one of the new mechanisms prevents the new GA from reaching a certain condition under which the original GA does not converge. The results of experiments show that the improved GA fulfills those goals.
Year
DOI
Venue
2014
10.1109/SCCC.2014.12
2014 33rd International Conference of the Chilean Computer Science Society (SCCC)
Keywords
Field
DocType
Agent-based modelling,genetic algorithms,calibration,validation,relational equivalence,complex adaptive systems
Convergence (routing),Time series,Computer science,Artificial intelligence,Complex adaptive system,Calibration,Machine learning,Genetic algorithm
Conference
ISSN
ISBN
Citations 
1522-4902
978-1-5090-0422-5
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Enrique Canessa1339.82
Sergio E. Chaigneau235.69