Genetic-Programming-Based Symbolic Regression for Heat Transfer Correlations of a Compact Heat Exchanger

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Weihua Cai and Mihir Sen and K. T. Yang and 
                 Arturo Pacheco-Vega",
  title =        "Genetic-Programming-Based Symbolic Regression for Heat
                 Transfer Correlations of a Compact Heat Exchanger",
  booktitle =    "ASME Summer Heat Transfer Conference (HT2005)",
  year =         "2005",
  volume =       "4",
  pages =        "367--374",
  address =      "San Francisco, California, USA",
  month =        jul # " 17-22",
  publisher =    "ASME",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7918-4734-9",
  DOI =          "doi:10.1115/HT2005-72293",
  abstract =     "We describe a symbolic regression methodology based on
                 genetic programming to find correlations that can be
                 used to estimate the performance of compact heat
                 exchangers. Genetic programming is an evolutionary
                 search technique in which functions represented as
                 parse trees evolve as the search proceeds. An advantage
                 of this approach is that functional forms of the
                 correlation need not be assumed. The algorithm performs
                 symbolic regression by seeking both the functional
                 structure of the correlation and the coefficients
                 therein that enable the closest fit to experimental
                 data. This search is conducted within a functional
                 domain constructed from sets of operators and terminals
                 that are used to build tree-structures representing
                 functions. A penalty function is used to prevent large
                 correlations. The methodology is tested using first
                 artificial data from a one-dimensional function and
                 later a set of published heat exchanger experiments.
                 Comparison with published results from the same data
                 show that symbolic-regression correlations are as good
                 or better. The effect of the penalty parameters on the
                 best function is also analysed.",
  notes =        "collocated with the ASME 2005 Pacific Rim Technical
                 Conference and Exhibition on Integration and Packaging
                 of MEMS, NEMS, and Electronic Systems (HT2005)
                 University of Notre Dame, Notre Dame, IN",

Genetic Programming entries for Weihua Cai Mihir Sen K T Yang Arturo Javier Pacheco Vega