- 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/biologie/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",
