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  <titleInfo>
    <nonSort>A </nonSort>
    <title>genetic approach to class scheduling</title>
  </titleInfo>
  <name type="personal">
    <namePart>Le Minh Tuan</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Nagarur, Nagendra N.</namePart>
    <role>
      <roleTerm type="text">Chairperson</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Pastijn, Hugo</namePart>
    <role>
      <roleTerm type="text">Examination committee</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Do, Ba Khang</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="corporate">
    <namePart>Government of Belgium</namePart>
    <role>
      <roleTerm type="text">Scholarship donor</roleTerm>
    </role>
  </name>
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  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">th</placeTerm>
    </place>
    <place>
      <placeTerm type="text">Bangkok</placeTerm>
    </place>
    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>1996</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
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    <extent>109 p.</extent>
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  <abstract>A genetic approach was applied for the solution of the model. The courses were grouped into  blocks, where each block represents a characteristic schedule of timeslot. The genetic  algorithm created a specific number of solutions and use the best features of these solutions to  form the offsprings. The developed genetic algorithm was used for solving the AIT class  scheduling problem. An implementation procedure is developed and described for using the  genetic algorithm software, and a detailed users manual is prepared for the software. The  algorithm is tested for its performance with respect to the population size, the number of  iterations, and the convergency. The developed algorithm is found to be very powerful and fast  in searching an optimal solution.</abstract>
  <note>A thesis submitted in partial fulfillment of the requirements  for the degree of Master of Engineering, School of Advanced Technologies  </note>
  <note>Thesis (M. Eng.) - Asian Institute of Technology, 1996</note>
  <subject authority="lcsh">
    <topic>Genetic algorithms</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Scheduling (Management)</topic>
  </subject>
  <relatedItem type="series">
    <titleInfo>
      <title>Thesis ; no. ISE-96-19</title>
    </titleInfo>
    <name type="corporate">
      <namePart>Asian Institute of Technology.</namePart>
      <namePart/>
    </name>
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  <identifier type="uri">http://203.159.5.9/ait-thesis/detail.php?q=B14804</identifier>
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    <url displayLabel="Full-Text">http://203.159.5.9/ait-thesis/detail.php?q=B14804</url>
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