A Model for Analyzing Black-Box Optimization

  • Vinhthuy Phan
  • Steven Skiena
  • Pavel Sumazin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2748)


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.


Local Search Tabu Search Travel Salesman Problem Vertex Cover Hill Climbing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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