Scientific Discovery using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@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