Worst-Case Analyses of Self-Organizing Sequential Search Heuristics.
CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
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The performance of sequential search can be enhanced by the use of heuristics that move elements closer to the front of the list as they are found. Previous analyses have characterized the performance of such heuristics probabilitically. In this paper we show that the heuristics can also be analyzed in the worst-case sense, and that the relative merit of the heuristics under this analysis is different than in the probabilistic analyses. Simulations show that the relative merit of the heuristics on real data is closer to that of the new worst-case analyses rather than that of the previous probabilistic analyses. Author
- Statistics and Probability