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Enhancing Stochastic Search Performance by Value-Biased Randomization of Heuristics

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Abstract

This paper investigates the utility of introducing randomization as a means of boosting the performance of search heuristics. We introduce a particular approach to randomization, called Value-biased stochastic sampling (VBSS), which emphasizes the use of heuristic value in determining stochastic bias. We offer an empirical study of the performance of value-biased and rank-biased approaches to randomizing search heuristics. We also consider the use of these stochastic sampling techniques in conjunction with local hill-climbing. Finally, we contrast the performance of stochastic sampling search with more systematic search procedures as a means of amplifying the performance of search heuristics.

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Authors and Affiliations

  1. Department of Computer Science, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104

    Vincent A. Cicirello

  2. The Robotics Insitute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213

    Stephen F. Smith

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  1. Vincent A. Cicirello
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  2. Stephen F. Smith
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Correspondence to Vincent A. Cicirello.

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Cicirello, V.A., Smith, S.F. Enhancing Stochastic Search Performance by Value-Biased Randomization of Heuristics. J Heuristics 11, 5–34 (2005). https://doi.org/10.1007/s10732-005-6997-8

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