Two-Fold Optimization by Evolutionary Computation and Simulated Annealing for Reconstructing Citizens' Attributes from Statistics

Abstract

In this paper, we propose a simulated annealing method with an evolutionary computation for reconstructing citizens’' attributes from statistics. To implement agent-based social simulations for real communities, it is needed to reconstruct citizens'’ attributes such as age, sex, income, occupation,academic background, and so on. Although such personal data are available in the local government,they are protected for privacy reason. In order to enable any body to implement agent-based social simulation, a reliable reconstruction method is required. In this paper, we modify a previous approach using simulated annealing by incorporating a method that minimizes errors between a generated population and the real statistics. Additionally we propose an evolutionary algorithm that is applied before the proposed modified simulated annealing. We show the effectivity of the proposed two-fold algorithm using some computational experiments.