Development of a hybrid genetic programming technique for computationally expensive optimisation problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  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 =          "",
  URL =          "",
  URL =          "",
  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
  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

Genetic Programming entries for Umberto Armani