Control of Physical Consistency in Metamodel Building by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Armani_2011_2,
  author =       "U. Armani and Z. Khatir and Amirul Khan and 
                 V. V. Toropov and A. Polynkin and H. Thompson and 
                 N. Kapur and C. J. Noakes",
  title =        "Control of Physical Consistency in Metamodel Building
                 by Genetic Programming",
  booktitle =    "Proceedings of the Second International Conference on
                 Soft Computing Technology in Civil, Structural and
                 Environmental Engineering (CSC2011)",
  year =         "2011",
  editor =       "Y. Tsompanakis and B. H. V. Topping",
  pages =        "Paper 43",
  address =      "Chania, Greece",
  publisher_address = "Stirlingshire, UK",
  publisher =    "Civil-Comp Press",
  keywords =     "genetic algorithms, genetic programming, high-fidelity
                 design optimisation, metamodel, mathematical structure,
                 non-linear system, analytical expression, engineering
                 applications",
  URL =          "http://www.ctresources.info/ccp/paper.html?id=6631",
  DOI =          "doi:10.4203/ccp.97.43",
  size =         "18 pages",
  abstract =     "Soft computing has grown in importance in recent
                 years, allowing engineers to handle more and more
                 complex problems. Computer power has made different
                 classes of computationally intensive techniques viable
                 and successful alternatives to other established
                 methods. Algorithms based on machine learning, data
                 mining and genetically inspired methods are in some
                 cases the only choice when the knowledge of the problem
                 is scarce.

                 Genetic programming (GP) [1] can be considered one of
                 the latest techniques to have appeared in the range of
                 soft computing tools. It is a genetically-inspired
                 method able to generate from a data set global
                 metamodels describing the relationship between a
                 system's input and output data. Typically, genetic
                 operators are used to recombine parts of mathematical
                 expressions in a randomised but directed way until a
                 high quality metamodel (i.e. a model of a model) is
                 found. The major strength of genetic programming lies
                 in its ability to provide explicit metamodels, making
                 possible the use of traditional analytical methods for
                 the subsequent analysis and optimisation.

                 A problem arises that the stochastic nature of GP
                 reduces the possibility of controlling the consistency
                 of the generated metamodels. It is not uncommon in a
                 conventional GP experiment to obtain expressions that
                 despite showing low errors cannot be used in an
                 application as their response is not consistent with
                 the assumptions imposed by the problem's nature.

                 In this paper it is described how control of the
                 physical consistency of the generated metamodels can be
                 improved using some basic knowledge regarding the
                 problem at hand by imposing constraints in the problem
                 formulation. The benefits of the new strategy are shown
                 through a benchmark problem. Two case studies where
                 genetic programming has been successfully applied to
                 optimise the ventilation design of an industrial bread
                 baking oven and of a hospital ward are also presented.
                 In both cases data provided by computational fluid
                 dynamics (CFD) simulations were used to generate a
                 metamodel and genetic algorithm techniques were used to
                 find the optimum of the modelled response. Validation
                 of the optimal point performed using data generated by
                 additional CFD simulations confirmed the high quality
                 of the metamodels. In a case study the optimum found by
                 genetic programming matches the optimum found by
                 another metamodelling technique.",
}

Genetic Programming entries for Umberto Armani Z Khatir Amirul Khan Vassili V Toropov Andrey Polynkin H Thompson N Kapur C J Noakes

Citations