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lars gislen

Lars Gislén

Retired, affiliated researcher

lars gislen

Complex Scheduling with Potts Neural Networks

Author

  • Lars Gislén
  • Carsten Peterson
  • Bo Söderberg

Summary, in English

In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the interaction of Potts neurons with different number of components. We analyze the corresponding linearized mean field equations with respect to estimating the phase transition temperature. Also a brief comparison with the linear programming approach is given. Testbeds consisting of generated problems within the Swedish high school system are solved efficiently with high quality solutions as results.

Department/s

  • Computational Biology and Biological Physics - Undergoing reorganization

Publishing year

1992

Language

English

Pages

805-831

Publication/Series

Neural Computation

Volume

4

Issue

6

Document type

Journal article

Publisher

MIT Press

Topic

  • Computer and Information Science

Status

Published

ISBN/ISSN/Other

  • ISSN: 1530-888X