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Software for Implementing the Sequential Elimination of Level Combinations Algorithm

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  • Johnson, Kjell
  • Mandal, Abhyuday
  • Ding, Tan

Abstract

Genetic algorithms (GAs) are a popular technology to search for an optimum in a large search space. Using new concepts of forbidden array and weighted mutation, Mandal, Wu, and Johnson (2006) used elements of GAs to introduce a new global optimization technique called sequential elimination of level combinations (SELC), that efficiently finds optimums. A SAS macro, and MATLAB and R functions are developed to implement the SELC algorithm.

Suggested Citation

  • Johnson, Kjell & Mandal, Abhyuday & Ding, Tan, 2008. "Software for Implementing the Sequential Elimination of Level Combinations Algorithm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i06).
    • Handle: RePEc:jss:jstsof:v:025:i06
    DOI: http://hdl.handle.net/10.18637/jss.v025.i06
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    References listed on IDEAS

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    1. Hamada M. & Martz H. F. & Reese C. S. & Wilson A. G., 2001. "Finding Near-Optimal Bayesian Experimental Designs via Genetic Algorithms," The American Statistician, American Statistical Association, vol. 55, pages 175-181, August.
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