Evolving genetic algorithm for Job Shop Scheduling problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "James C. Werner and Mehmet E. Aydin and 
                 Terence C. Fogarty",
  title =        "Evolving genetic algorithm for Job Shop Scheduling
  institution =  "London South Bank University",
  year =         "2001",
  address =      "School of Computing, Information Systems and
                 Mathematics, South Bank University, 103 Borough Road,
                 London SE1 0AA, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.geocities.com/jamwer2002/rep1.pdf",
  size =         "5 pages",
  abstract =     "an attempt to evolve genetic algorithms by a
                 particular genetic programming method to make it able
                 to solve the classical Job Shop Scheduling problem
                 (JSSP), which is a type of very well known hard
                 combinatorial optimisation problems. The aim is to look
                 for a better GA such that solves JSSP with preferable
                 scores. This looking up procedure is done by evolving
                 GA with GP. First we solve a set of job shop scheduling
                 benchmarks by using a conventional GA and then an
                 association of GP to evolve a GA. The instance of JSSP
                 tackled are available in OR literature.",

Genetic Programming entries for James Cunha Werner Mehmet Emin Aydin Terence C Fogarty