統計データからの市民の属性復元のための進化計算とSAによる2段階最適化

書誌事項

タイトル別名
  • Two-Fold Optimization by Evolutionary Computation and Simulated Annealing for Reconstructing Citizens' Attributes from Statistics
  • トウケイ データ カラ ノ シミン ノ ゾクセイ フクゲン ノ タメ ノ シンカ ケイサン ト SA ニ ヨル 2 ダンカイ サイテキカ

この論文をさがす

抄録

<p>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.</p>

収録刊行物

被引用文献 (2)*注記

もっと見る

参考文献 (3)*注記

もっと見る

関連プロジェクト

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ