Two-Fold Optimization by Evolutionary Computation and Simulated Annealing for Reconstructing Citizens' Attributes from Statistics
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- Masui Daiki
- Graduate School of Informatics, Kansai University
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- Murata Tadahiko
- Faculty of Informatics, Kansai University
Bibliographic Information
- Other Title
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- 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
- 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
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Abstract
<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>
Journal
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- Transactions of the Institute of Systems, Control and Information Engineers
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Transactions of the Institute of Systems, Control and Information Engineers 30 (6), 216-227, 2017
THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)
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Details 詳細情報について
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- CRID
- 1390001205167194880
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- NII Article ID
- 130006078705
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- NII Book ID
- AN1013280X
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- ISSN
- 2185811X
- 13425668
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- NDL BIB ID
- 028285591
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed
