Noisy GA resampling on evolved parameterized policies for stochastic constraint programming

Jing Tian, Tomohiro Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012
Pages1439-1442
Number of pages4
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 International Conference on Computer Science and Service System, CSSS 2012 - Nanjing, China
Duration: 2012 Aug 112012 Aug 13

Publication series

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

Conference

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

Keywords

  • Evolved Parameterized Policies
  • Noisy GA
  • Resampling/Sampling
  • Stochastic Constraint Programming

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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  • Cite this

    Tian, J., & Murata, T. (2012). Noisy GA resampling on evolved parameterized policies for stochastic constraint programming. In Proceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012 (pp. 1439-1442). [6394600] (Proceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012). https://doi.org/10.1109/CSSS.2012.362