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Comparison of methods for developing dynamic reduced models for design optimization

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Abstract

In this paper we compare three methods for forming reduced models to speed up genetic-algorithm-based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization by making the genetic operators more informed. Empirical results in several engineering design domains are presented.

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Authors and Affiliations

  1. Department of Computer Science, The University of Georgia, Athens, GA, 30602, USA

    K. Rasheed

  2. Department of Statistics, North Carolina, State University, Raleigh, NC, 27695, USA

    X. Ni

  3. College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    S. Vattam

Authors
  1. K. Rasheed
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  2. X. Ni
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  3. S. Vattam
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Corresponding author

Correspondence to K. Rasheed.

Additional information

This research was funded in part by a sub-contract from the Rutgers-based Self Adaptive Software project supported by the Advanced Research Projects Agency of the Department of Defense and by NASA under grant NAG2-1234.

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Rasheed, K., Ni, X. & Vattam, S. Comparison of methods for developing dynamic reduced models for design optimization. Soft Computing 9, 29–37 (2005). https://doi.org/10.1007/s00500-003-0331-x

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  • DOI: https://doi.org/10.1007/s00500-003-0331-x

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