- author = "Umberto Armani",
- title = "Development of a hybrid genetic programming technique for computationally expensive optimisation problems",
- school = "School of Civil Engineering, University of Leeds",
- year = "2014",
- address = "UK",
- month = feb,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://etheses.whiterose.ac.uk/7281/1/Armani_PhD_thesis_resubmission_grerrors_corrected.pdf",
-
URL = "
http://etheses.whiterose.ac.uk/7281/",
-
URL = "http://ethos.bl.uk/OrderDetails.do?did=50&uin=uk.bl.ethos.631392",
- size = "406 pages",
-
abstract = "The increasing computational power of modern computers
has contributed to the advance of nature-inspired
algorithms in the fields of optimisation and
metamodelling. Genetic programming (GP) is a
genetically-inspired technique that can be used for
meta modelling purposes. GP main strength is in the
ability to infer the mathematical structure of the best
model fitting a given data set, relying exclusively on
input data and on a set of mathematical functions given
by the user. Model inference is based on an iterative
or evolutionary process, which returns the model as a
symbolic expression (text expression). As a result,
model evaluation is inexpensive and the generated
expressions can be easily deployed to other users.
Despite genetic programming has been used in many
different branches of engineering, its diffusion on
industrial scale is still limited. The aims of this
thesis are to investigate the intrinsic limitations of
genetic programming, to provide a comprehensive review
of how researchers have tackled genetic programming
main weaknesses and to improve genetic programming
ability to extract accurate models from data. In
particular, research has followed three main
directions. The first has been the development of
regularisation techniques to improve the generalisation
ability of a model of a given mathematical structure,
based on the use of a specific tuning algorithm in case
sinusoidal functions are among the functions the model
is composed of. The second has been the analysis of the
influence that prior knowledge regarding the function
to approximate may have on genetic programming
inference process. The study has led to the
introduction of a strategy that allows to use prior
knowledge to improve model accuracy. Thirdly, the
mathematical structure of the models returned by
genetic programming has been systematically analysed
and has led to the conclusion that the linear
combination is the structure that is mostly returned by
genetic programming runs. A strategy has been
formulated to reduce the evolutionary advantage of
linear combinations and to protect more complex classes
of individuals throughout the evolution.
The possibility to use genetic programming in industrial optimisation problems has also been assessed with the help of a new genetic programming implementation developed during the research activity. Such implementation is an open source project and is freely downloadable from http://www.personal.leeds.ac.uk/~cnua/mypage.html.",
- notes = "HyGP C.4 Hock function C.5 Branin-Hoo function C.6 Rosenbrock function (PCE comparison) C.7 Kotanchek function (PCE comparison) C.8 10-bar truss optimisation C.9 Hospital ward ventilation optimisation C.10 Chromate diffusion model C.11 Jet pump model C.12 Bread baking oven design optimisation C.13 Aerodynamic optimisation of NASA rotor 37 compressor rotor blade uk.bl.ethos.631392",
