Automatic generation of heuristics for scheduling

  • Authors:

  • Robert A. Morris;John L. Bresina;Stuart M. Rodgers

  • Affiliations:

  • Florida Institute of Technology, Melbourne, FL;Recom Technologies, NASA Ames Research Center, Moffett Field, CA;Computer Science Department, University of West Florida, Pensacola, FL

  • Venue:
  • IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
  • Year:
  • 1997

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Abstract

This paper presents a technique, called GENH, that automatically generates search heuristics for scheduling problems. The impetus for developing this technique is the growing consensus that heuristics encode advice that is, at best, useful in solving most, or typical, problem instances, and, at worst, useful in solving only a narrowly defined set of instances. In either case, heuristic problem solvers, to be broadly applicable, should have a means of automatically adjusting to the idiosyncrasies of each problem instance. GENH generates a search heuristic for a given problem instance by hillclimbing in the space of possible multiattribute heuristics, where the evaluation of a candidate heuristic is based on the quality of the solution found under its guidance. We present empirical results obtained by applying GENH to the real world problem of telescope observation scheduling. These results demonstrate that GENH is a simple and effective way of improving the performance of an heuristic scheduler.