Parametric Regression Through Genetic Programming

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  author =       "Edwin Roger Banks and James Hayes and Edwin Nunez",
  title =        "Parametric Regression Through Genetic Programming",
  booktitle =    "Late Breaking Papers at the 2004 Genetic and
                 Evolutionary Computation Conference",
  year =         "2004",
  editor =       "Maarten Keijzer",
  address =      "Seattle, Washington, USA",
  month =        "26 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  abstract =     "Parametric regression in genetic programming can
                 substantially speed up the search for solutions.
                 Paradoxically, the same technique has difficulty
                 finding a true optimum solution. The parametric
                 formulation of a problem results in a fitness landscape
                 that looks like an inverted brush with many bristles of
                 almost equal length (individuals of high fitness), but
                 with only one bristle that is very slightly longer than
                 the rest, the optimum solution. As such it is easy to
                 find very good, even outstanding solutions, but very
                 difficult to locate the optimum solution. In this paper
                 parametric regression is applied to a
                 minimum-time-to-target problem. The solution is
                 equivalent to the classical brachistochrone. Two
                 formulations were tried: a parametric regression and
                 the classical symbolic regression formulation. The
                 parametric approach was superior without exception. We
                 speculate the parametric approach is more generally
                 applicable to other problems and suggest areas for more
  notes =        "Part of \cite{keijzer:2004:GECCO:lbp}",

Genetic Programming entries for Edwin Roger Banks James C Hayes Edwin Nunez