Noisy GA resampling on evolved parameterized policies for stochastic constraint programming

Jing Tian*, Tomohiro Murata

*この研究の対応する著者

研究成果: Conference contribution

抄録

Stochastic Constraint Programming is an extension of Constraint Programming for modeling and solving combinatorial problems which involve uncertainty in real world. Evolved Parameterized Policies (EPP) is the first incomplete approach to stochastic constraint problems which has higher performance rather than other methods, but still seems non-practical for large multi-stage problems due to scenarios exponentially growing. We proposed new resampling method called IDGAS based on Noisy GAs and other Evolutionary Computation algorithms, which aim to ensure the reliability while keeping in high search performance. In experiments on credit portfolio management with multi-stage, it performed more effective than conventional EPP and other resampling methods.

本文言語English
ホスト出版物のタイトルProceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012
ページ1439-1442
ページ数4
DOI
出版ステータスPublished - 2012 12 1
イベント2012 International Conference on Computer Science and Service System, CSSS 2012 - Nanjing, China
継続期間: 2012 8 112012 8 13

出版物シリーズ

名前Proceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012

Conference

Conference2012 International Conference on Computer Science and Service System, CSSS 2012
国/地域China
CityNanjing
Period12/8/1112/8/13

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)

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