Scientific Discovery using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @PhdThesis{keijzer:2001:thesis,
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author = "Maarten Keijzer",
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title = "Scientific Discovery using Genetic Programming",
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school = "Danish Technical University, IMM, Institute for
Mathematical Modelling, Digital Signal Processing
group",
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year = "2002",
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address = "DK-2800 Lyngby, Denmark",
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month = mar,
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0909-3192",
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URL = "http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/797/ps/imm797.ps",
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URL = "http://www.cs.vu.nl/~mkeijzer/publications/thesis.ps.gz",
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broken = "http://www.cs.vu.nl/~mkeijzer/publications/thesis/",
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size = "173 pages",
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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.",
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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",
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notes = "Supervisors: Lars Kai Hansen and Vladan Babovic",
- }
Genetic Programming entries for
Maarten Keijzer
Citations