Efficient Generation of Near Optimal Initial Populations to Enhance Genetic Algorithms for Job-Shop Scheduling

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@Article{Kuczapski:2010:ITC,
  author =       "Artur M. Kuczapski and Mihai V. Micea and 
                 Laurentiu A. Maniu and Vladimir I. Cretu",
  title =        "Efficient Generation of Near Optimal Initial
                 Populations to Enhance Genetic Algorithms for Job-Shop
                 Scheduling",
  journal =      "Information Technology and Control",
  year =         "2010",
  volume =       "39",
  number =       "1",
  pages =        "32--37",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1392-124X",
  URL =          "http://www.itc.ktu.lt/index.php/ITC/article/view/12091",
  size =         "6 pages",
  abstract =     "This paper presents an efficient method of enhancing
                 genetic algorithms (GAs) for solving the Job-Shop
                 Scheduling Problem (JSSP), by generating near optimal
                 initial populations. Since the choice of the initial
                 population has a high impact on the speed of the
                 evolution and the quality of the final results, we
                 focused on generating its individuals using genetically
                 evolved priority dispatching rules. Our experiments
                 show a significant increase in quality and speed of
                 scheduling with GAs, and in some cases the evolved
                 priority rules alone determined better solutions then
                 the GA itself. The analysed reference GA uses Giffler
                 and Thompson (GT) heuristic and priority lists. To
                 speed up the generation of priority rules, we have used
                 a weighted sum of priority rules formula that revealed
                 significantly better performances than Genetic
                 Programming (GP). For evaluation of the proposed
                 algorithm, the well known benchmark data sets from
                 Fisher and Thompson (F&T) and Laurence Kramer (LA) have
                 been used.",
  notes =        "

                 Politehnica University of Timisoara",
}

Genetic Programming entries for Artur M Kuczapski Mihai V Micea Laurentiu A Maniu Vladimir I Cretu

Citations