Declarative and Preferential Bias in GP-based Scientific Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{keijzer:2002:GPEM,
  author =       "Maarten Keijzer and Vladan Babovic",
  title =        "Declarative and Preferential Bias in GP-based
                 Scientific Discovery",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "1",
  pages =        "41--79",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, symbolic
                 regression, strong typing, coercion typing, empirical
                 equations, hydraulics",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1023/A:1014596120381",
  abstract =     "This work examines two methods for evolving
                 dimensionally correct equations on the basis of data.
                 It is demonstrated that the use of units of measurement
                 aids in evolving equations that are amenable to
                 interpretation by domain specialists. One method uses a
                 strong typing approach that implements a declarative
                 bias towards correct equations, the other method uses a
                 coercion mechanism in order to implement a preferential
                 bias towards the same objective. Four experiments using
                 real-world, unsolved scientific problems were performed
                 in order to examine the differences between the
                 approaches and to judge the worth of the induction
                 methods. Not only does the coercion approach perform
                 significantly better on two out of the four problems
                 when compared to the strongly typed approach, but it
                 also regularizes the expressions it induces, resulting
                 in a more reliable search process. A trade-off between
                 type correctness and ability to solve the problem is
                 identified. Due to the preferential bias implemented in
                 the coercion approach, this trade-off does not lead to
                 sub-optimal performance. No evidence is found that the
                 reduction of the search space achieved through
                 declarative bias helps in finding better solutions
                 faster. In fact, for the class of scientific discovery
                 problems the opposite seems to be the case.",
  notes =        "Article ID: 395989",
}

Genetic Programming entries for Maarten Keijzer Vladan Babovic

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