Learning Distributed Reactive Strategies by Genetic Programming for the General Job Shop Problem   [GP]

by

Atlan, L., Bonnet, J. and Naillon, M.

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Info: Proceedings of the 7th annual Florida Artificial Intelligence Research Symposium (Conference proceedings), 1994
Keywords:genetic algorithms, genetic programming
Abstract:
proposed is a general system to infer symbolic policy functions for distributed reactive scheduling in non-stationary environments. [NE] The job shop problem is only used as a validating case study. Our system is based both on an original distributed scheduling model and on genetic programming [GP] for the inference of symbolic policy functions. The purpose is to determine heuristic policies that are local in time, long term near-optimal, and robust with respect to perturbations. Furthermore, the policies are local in state space: the global decision problem is split into as many decision problems as there are agents, i.e. machines in the job shop problem. If desired, the genetic algorithm [GA] can use expert knowledge as a priori knowledge, via implementation of the symbolic representation of the policy functions.
Notes:
"To be published in the proceedings of the Seventh Annual Florida Artificial Intelligence Research Symposium" DGT/DEA/IA2 December 1993 Combination of GP and Giffler and Thompson algorithm
URL(s):(G)zipped postscript

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BibTex:
@InProceedings{atlan:1994:gpjss,
  author =       "Laurent Atlan and Jerome Bonnet and Martine Naillon",
  title =        "Learning Distributed Reactive Strategies by Genetic
                 Programming for the General Job Shop Problem",
  booktitle =    "Proceedings of the 7th annual Florida Artificial
                 Intelligence Research Symposium",
  year =         "1994",
  address =      "Pensacola, Florida, USA",
  month =        may,
  organisation = "Dassault-Aviation, Artificial Intelligence
                 Department",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.ens.fr/pub/reports/bioligie/disgajsp.ps.Z",
  size =         "11 pages",
  abstract =     "proposed is a general system to infer symbolic policy
                 functions for distributed reactive scheduling in
                 non-stationary environments. The job shop problem is
                 only used as a validating case study. Our system is
                 based both on an original distributed scheduling model
                 and on genetic programming for the inference of
                 symbolic policy functions. The purpose is to determine
                 heuristic policies that are local in time, long term
                 near-optimal, and robust with respect to perturbations.
                 Furthermore, the policies are local in state space: the
                 global decision problem is split into as many decision
                 problems as there are agents, i.e. machines in the job
                 shop problem. If desired, the genetic algorithm can use
                 expert knowledge as a priori knowledge, via
                 implementation of the symbolic representation of the
                 policy functions.",
  notes =        "{"}To be published in the proceedings of the Seventh
                 Annual Florida Artificial Intelligence Research
                 Symposium{"} DGT/DEA/IA2 December 1993

                 Combination of GP and Giffler and Thompson algorithm",
}