Scientific Discovery using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{keijzer:2001:thesis,
  author =       "Maarten Keijzer",
  title =        "Scientific Discovery using Genetic Programming",
  school =       "Danish Technical University, IMM, Institute for
                 Mathematical Modelling, Digital Signal Processing
                 group",
  year =         "2002",
  address =      "DK-2800 Lyngby, Denmark",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0909-3192",
  URL =          "http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/797/ps/imm797.ps",
  URL =          "http://www.cs.vu.nl/~mkeijzer/publications/thesis.ps.gz",
  broken =       "http://www.cs.vu.nl/~mkeijzer/publications/thesis/",
  size =         "173 pages",
  abstract =     "Genetic Programming is capable of automatically
                 inducing symbolic computer programs on the basis of a
                 set of examples or their performance in a simulation.
                 Mathematical expressions are a well-defined subset of
                 symbolic computer programs and are also suitable for
                 optimization using the genetic programming paradigm.
                 The induction of mathematical expressions based on data
                 is called symbolic regression. In this work, genetic
                 programming is extended to not just fit the data i.e.,
                 get the numbers right, but also to get the dimensions
                 right. For this units of measurement are used. The main
                 contribution in this work can be summarized as: The
                 symbolic expressions produced by genetic programming
                 can be made suitable for analysis and interpretation by
                 using units of measurement to guide or restrict the
                 search. To achieve this, the following has been
                 accomplished: A standard genetic programming system is
                 modified to be able to induce expressions that
                 more-or-less abide type constraints. This system is
                 used to implement a preferential bias towards
                 dimensionally correct solutions. A novel genetic
                 programming system is introduced that is able to induce
                 expressions in languages that need context-sensitive
                 constraints. It is demonstrated that this system can be
                 used to implement a declarative bias towards 1.the
                 exclusion of certain syntactical constructs; 2.the
                 induction of expressions that use units of measurement;
                 3.the induction of expressions that use matrix algebra;
                 4.the induction of expressions that are numerically
                 stable and correct. A case study using four real-world
                 problems in the induction of dimensionally correct
                 empirical equations on data using the two different
                 methods is presented to illustrate the use and
                 limitations of these methods in a framework of
                 scientific discovery.",
  abstract =     "Genetisk programmering er i stand til at producere
                 computer programmer, automatisk pa baggrund af
                 eksempler pa programmernes virkning i en simulering. Da
                 matematiske udtryk er en veldefineret delmangde af
                 symbolske computer programmer og kan disse ogsa
                 bestemmes under genetisk programmerings paradigmet.
                 Empirisk bestemmelse af matematiske udtryk kaldes
                 symbolsk regression. I dette arbejde bliver genetisk
                 programmering udvidet til, et varktoj der ikke bare
                 {"}fitter data{"}, men ogsa giver korrekte fysiske
                 dimensioner. De vasentligste bidrag i dette arbejde
                 opsummeres ved: Symbolske udtryk, udledt ved hjalp af
                 genetisk programmering kan gores tilgangelige for
                 analyse og fortolkning, ved at lade
                 dimensionsbetragtninger stotte eller begranse
                 sogerummet. Dette er opnaet ved at Et standard genetisk
                 programmerings-varktoj er blevet modificeret til at
                 producerer udtryk som hovedsagligt er dimensionelt
                 konsistente. Dette modificerede system er anvendt til
                 at malrette genetisk sogning mod dimensionelt korrekte
                 udtryk via sakaldt {"}preferential bias{"}. Et nyt
                 genetisk programmeringsvarktoj er blevet introduceret,
                 som kan producere udtryk baseret pa kontekst-folsomme
                 bibetingelser. Det er blevet demonstreret at dette
                 system kan implementere malrettet sogning som via
                 sakaldt {"}declarative bias{"} giver mulighed for at",
  notes =        "Supervisors: Lars Kai Hansen and Vladan Babovic",
}

Genetic Programming entries for Maarten Keijzer

Citations