Evolutionary Modelling of Industrial Systems with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Can:thesis,
  author =       "Birkan Can",
  title =        "Evolutionary Modelling of Industrial Systems with
                 Genetic Programming",
  school =       "University of Limerick",
  year =         "2011",
  type =         "Doctorate of Philosophy in Engineering",
  address =      "Ireland",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://hdl.handle.net/10344/1693",
  URL =          "http://ulir.ul.ie/handle/10344/1693",
  URL =          "http://ulir.ul.ie/bitstream/handle/10344/1693/2010_Birkan%2c%20Can.pdf",
  size =         "216 pages",
  abstract =     "Knowledge, experience, and intuition are integral
                 parts of decision making. However, these alone are not
                 sufficient to manage today's industrial systems. Often
                 predictive models are required to weigh options and
                 determine potential changes which provide the best
                 outcome for a system. In this respect, the dissertation
                 develops approximate models, metamodels, of industrial
                 systems to facilitate a means to quantify system
                 performance when the trade-off between approximation
                 error and efficiency (time and effort spent on model
                 development, validation, maintenance and execution) is
                 appropriate. Discrete-event simulation (DES) is widely
                 used to assist decision makers in the management of
                 systems. DES facilitates analysis with high fidelity
                 models as a consequence of its flexibility. However,
                 this descriptiveness introduces an overhead to model
                 building and maintenance. Furthermore, due to
                 stochastic elements and the size of the systems
                 modelled, model execution times can be computationally
                 demanding. Hence, its use in operational tasks such as
                 design, sensitivity analysis and optimisation can be
                 significantly undermined when efficiency is a concern.
                 In this thesis, these shortcomings are addressed
                 through research into the use of genetic programming
                 for metamodelling.

                 Genetic programming is a branch of evolutionary
                 algorithms which emulate the natural evolution of
                 species. It can evolve programs of a domain via
                 symbolic regression. These programs can be interpreted
                 as logic instructions, analytical functions etc.
                 Furthermore, genetic programming develops the models
                 without prior assumptions about the underlying function
                 of the training data. This can provide significant
                 advantage for modelling of complex systems with
                 non-linear and multimodal response characteristics.
                 Exploiting these properties, the dissertation presents
                 research towards developing metamodels of manufacturing
                 systems (or their DES models) via genetic programming
                 in the context of symbolic regression. In particular,
                 it contributes to; (i) exploration of an appropriate
                 experimental design method suitable to use with genetic
                 programming, (ii) to a comparison of the performance of
                 genetic programming with neural networks, using three
                 different stochastic industrial problems to identify
                 its robustness; (iii) research into an improved genetic
                 programming and dynamic flow time estimation.",
  notes =        "2011 Supervisor Dr. Cathal Heavey.

                 'thesis is available in the University Library'

                 ",
}

Genetic Programming entries for Birkan Can

Citations