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    <title>genetic algorithm for selecting optimal parameters in the Meshless Local Petrov-Galerkin method</title>
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  <name type="personal">
    <namePart>Tanan Chub-uppakarn</namePart>
    <role>
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  <name type="personal">
    <namePart>Barry, William Joseph</namePart>
    <role>
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  <name type="personal">
    <namePart>Worsak Kanok-Nukulchai</namePart>
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  <name type="personal">
    <namePart>Pennung Warnitchai</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="corporate">
    <namePart>RTG Fellow Scholarship</namePart>
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  <genre authority="marc">technical report</genre>
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    <place>
      <placeTerm type="text">Bangkok</placeTerm>
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    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>2003</dateIssued>
    <issuance>continuing</issuance>
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  <abstract>Meshless methods have become very attractive and efficient for the development of adaptive methods for solving boundary value problems because nodes can be easily added and removed without a burdensome remeshing of element. The meshless local PetrovGalerkin method approach, based on the local symmetric weak form and the moving least squares approximation, is a truly numerical meshless method for solving boundary value problems. This work employs polygonal nodal sub-domains constructed from several triangular patches rather than the typically used circular sub-domains is presented. The moving least-squares approximation and lagrange interpolation functions are used as the trial function and test functions respectively. This method aims to decrease the error of solution but many parameters of MLPG have an effect to the accuracy of the numerical solution such as x and y coordinates of nodal points, radius of influence. The presence of many parameters and complex process for obtaining the solution make finding difficult or possibility to solve using traditional algorithms. GAs has emerged as an efficient technique in handling for many parameters and complex process for obtaining the solution of elastostatic 2D problem. GAs is a search optimal solution process which is set by objective functions. The objective function consists of the L2 norm of displacement error and the ratio between gauss integration points in problem domain with node have effect on this gauss point integration because aim of this work want to reduce computation time and increase accuracy.</abstract>
  <note>A thesis submitted in partial fulfillment of the thesis requirement for the degree of Master of Engineering, School of Engineering and Technology</note>
  <note>Thesis (M.Eng.) - Asian Institute of Technology, 2003</note>
  <subject authority="lcsh">
    <topic>Genetic algorithms</topic>
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    <titleInfo>
      <title>Thesis ; no. ST-03-17</title>
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      <namePart>Asian Institute of Technology.</namePart>
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