Finding improved simulated annealing schedules with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Thonemann:1994:SAGP,
  author =       "Ulrich Wilhelm Thonemann",
  title =        "Finding improved simulated annealing schedules with
                 genetic programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "391--395",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  DOI =          "doi:10.1109/ICEC.1994.349919",
  keywords =     "genetic algorithms, genetic programming, simulated
                 annealing, quadratic assignment problem QAP,
                 combinatorial optimisation problem, heuristics, optimal
                 annealing schedule, performance, quadratic assignment
                 problem, combinatorial mathematics, optimisation,
                 scheduling simulated annealing",
  size =         "5 pages",
  abstract =     "Many combinatorial problems are too difficult to be
                 solved optimally, and hence heuristics are used to
                 obtain good solutions in reasonable time. A heuristic
                 that has been successfully applied to a variety of
                 problems is simulated annealing. However, the
                 performance of simulated annealing strongly depends on
                 the appropriate choice of a key parameter, the
                 annealing schedule. Usually, researchers experiment
                 with a number of manually created annealing schedules
                 and then choose the one that performs best for their
                 algorithms. This work applies genetic programming to
                 replace this manual search. For a given problem, we
                 search for an optimal annealing schedule. We
                 demonstrate the potential of this new approach by
                 optimising the annealing schedule for one of the
                 hardest combinatorial optimisation problem, the
                 quadratic assignment problem. We introduce a new
                 algorithm for solving the quadratic assignment problem
                 that performs extremely well, and we outline properties
                 of good annealing schedules",
  notes =        "Uses GP to generate cooling schedule for simulated
                 annealing. Demonstrates this on a series of QAP and
                 compares very favourably with published QAP results. GP
                 fitness found by running simulated annealing, so end up
                 doing loads of work. Best cooling schedules found are
                 problem dependant but several are highly oscillatory
                 and most don't drop to zero!",
}

Genetic Programming entries for Ulrich Thonemann

Citations