Genetic Programming Applications in Chemical Sciences and Engineering

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{Vyas:2015:hbgpa,
  author =       "Renu Vyas and Purva Goel and Sanjeev S. Tambe",
  title =        "Genetic Programming Applications in Chemical Sciences
                 and Engineering",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "5",
  pages =        "99--140",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, Classification, Chemical sciences and
                 engineering, Computational intelligence",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_5",
  abstract =     "Genetic programming (GP) (Koza, Genetic programming: a
                 paradigm for genetically breeding populations of
                 computer programs to solve problems, Stanford
                 University, Stanford, 1990) was originally proposed for
                 automatically generating computer programs that would
                 perform pre-defined tasks. There exist two other
                 important GP applications, namely classification and
                 symbolic regression that are being used widely in
                 pattern recognition and data-driven modelling,
                 respectively. As compared to the classification, GP has
                 found more applications for its capability to
                 effectively perform symbolic regression (SR). Given an
                 input-output data set SR can search and optimize an
                 appropriate linear/non-linear data-fitting function and
                 all its parameters. The GP-based symbolic regression
                 (GPSR) offers an attractive avenue to extract
                 correlations, explore candidate models and provide
                 optimal solutions to the data-driven modeling problems.
                 Despite its novelty and effectiveness, GP—unlike
                 artificial neural networks and support vector
                 regression—has not seen an explosive growth in its
                 applications. Owing to the availability of feature-rich
                 and user-friendly software packages as also faster
                 computers (including parallel computing devices), there
                 has been a spate of research publications in recent
                 years exploiting the significant potential of GP for
                 diverse classification and modelling applications in
                 chemistry and related sciences and engineering.
                 Accordingly, this chapter provides a bird's eye-view of
                 the ever increasing applications of GP in the chemical
                 sciences and engineering with the objective of bringing
                 out its immense potential in solving diverse problems.
                 The present chapter not only focuses on the important
                 GP-applications but also offers guidelines to develop
                 optimal GP models. Additionally, a non-exclusive list
                 of GP software packages is provided.",
}

Genetic Programming entries for Renu Vyas Purva Goel Sanjeev S Tambe

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