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

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 ABM´s 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.