A Model for Analyzing Black-Box Optimization

  • Vinhthuy Phan
  • Steven Skiena
  • Pavel Sumazin
Conference paper

DOI: 10.1007/978-3-540-45078-8_37

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2748)
Cite this paper as:
Phan V., Skiena S., Sumazin P. (2003) A Model for Analyzing Black-Box Optimization. In: Dehne F., Sack JR., Smid M. (eds) Algorithms and Data Structures. WADS 2003. Lecture Notes in Computer Science, vol 2748. Springer, Berlin, Heidelberg

Abstract

The design of heuristics for NP-hard problems is perhaps the most active area of research in the theory of combinatorial algorithms. However, practitioners more often resort to local-improvement heuristics such as gradient-descent search, simulated annealing, tabu search, or genetic algorithms. Properly implemented, local-improvement heuristics can lead to short, efficient programs that yield reasonable solutions. Designers of efficient local-improvement heuristics must make several crucial decisions, including the choice of neighborhood and heuristic for the problem at hand. We are interested in developing a general methodology for predicting the quality of local-neighborhood operators and heuristics, given a time budget and a solution evaluation function.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Vinhthuy Phan
    • 1
  • Steven Skiena
    • 2
  • Pavel Sumazin
    • 3
  1. 1.Computer ScienceSUNY at Stony BrookStony BrookUSA
  2. 2.Computer ScienceState University of New York at Stony BrookStony BrookUSA
  3. 3.Computer SciencePortland State UniversityOregon

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