Using HPC and PGAs to optimize noisy computational models of cognition

Sue E. Kase, Frank E. Ritter, Michael Schoelles

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

Abstract

Cognitive modeling on high performance computing platforms is an emerging field. A preliminary analysis is presented on the use of parallel processing and genetic algorithms for optimizing the fit of non-linear, multivariable symbolic models of human cognition to experimental data. The effectiveness of this experimental optimization methodology is illustrated with a prototype model of a serial arithmetic task built in the ACT-R cognitive architecture. The results confirm that HPC-based optimization techniques could replace the manual optimization techniques used by cognitive modelers up until the present.

Original languageEnglish (US)
Title of host publicationInnovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering
Pages477-482
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event2007 International Conference on Systems, Computing Sciences and Software Engineering, SCSS 2007, Part of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering, CISSE 2007 - Bridgeport, CT, United States
Duration: Dec 3 2007Dec 12 2007

Publication series

NameInnovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering

Other

Other2007 International Conference on Systems, Computing Sciences and Software Engineering, SCSS 2007, Part of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering, CISSE 2007
CountryUnited States
CityBridgeport, CT
Period12/3/0712/12/07

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Control and Systems Engineering

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    Kase, S. E., Ritter, F. E., & Schoelles, M. (2008). Using HPC and PGAs to optimize noisy computational models of cognition. In Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering (pp. 477-482). (Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering). https://doi.org/10.1007/978-1-4020-8735-6_89