A Multi-objective Genetic Programming Approach to Uncover Explicit and Implicit Equations from Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Bing Wang and Hemant Singh and Tapabrata Ray",
  title =        "A Multi-objective Genetic Programming Approach to
                 Uncover Explicit and Implicit Equations from Data",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "1129--1136",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257016",
  abstract =     "Identification of implicit and explicit relationships
                 in a data is a generic problem commonly encountered in
                 many fields of science and engineering. In the case of
                 explicit relations, one is interested in identifying a
                 compact and an accurate predictor function i.e. y =
                 f(x), while in the implicit case, one is interested in
                 identifying an equation of the form f(x) = 0. In both
                 these classes of problems, one would need to search
                 through a space of mathematical expressions, while
                 minimizing some form of error metric. Such expressions
                 are commonly identified using genetic programming (GP).
                 While methods to uncover explicit equations have been
                 studied extensively in the literature, there have been
                 limited attempts to solve implicit cases. Since there
                 are infinite trivial implicit forms that can be
                 generated from a given set of data, the choice of an
                 appropriate error metric is critical in the context of
                 implicit equation mining. In this paper, we introduce a
                 multiobjective genetic programming approach (MOGPA) for
                 the solution of both classes of problems. The maximum
                 depth of a GP-tree is used as the first objective
                 reflecting the complexity/compactness of the
                 expressions, while the mean error, either in the
                 predictor variable or the implicit derivatives is used
                 as the second objective during the course of search.
                 The performance of the approach is illustrated using
                 four examples. The approach delivers expressions of
                 various complexities spanning a range of accuracy
                 levels in a single run, unlike single objective GP
                 formulations. It was able to identify more compact and
                 accurate explicit forms than those from previously
                 reported studies, and the correct, most compact
                 expressions for implicit cases.",
  notes =        "1025 hrs 15335 CEC2015",

Genetic Programming entries for Bing Wang Hemant Singh Tapabrata Ray