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A study of search algorithms’ optimization speed

Author

Listed:
  • Andrea Valsecchi

    (European Centre for Soft Computing)

  • Leonardo Vanneschi

    (Università di Milano-Bicocca
    Universidade Nova de Lisboa)

  • Giancarlo Mauri

    (Università di Milano-Bicocca)

Abstract

Search algorithms are often compared by the optimization speed achieved on some sets of cost functions. Here some properties of algorithms’ optimization speed are introduced and discussed. In particular, we show that determining whether a set of cost functions F admits a search algorithm having given optimization speed is an NP-complete problem. Further, we derive an explicit formula to calculate the best achievable optimization speed when F is closed under permutation. Finally, we show that the optimization speed achieved by some well-know optimization techniques can be much worse than the best theoretical value, at least on some sets of optimization benchmarks.

Suggested Citation

  • Andrea Valsecchi & Leonardo Vanneschi & Giancarlo Mauri, 2014. "A study of search algorithms’ optimization speed," Journal of Combinatorial Optimization, Springer, vol. 27(2), pages 256-270, February.
    • Handle: RePEc:spr:jcomop:v:27:y:2014:i:2:d:10.1007_s10878-012-9514-7
    DOI: 10.1007/s10878-012-9514-7
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    References listed on IDEAS

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    1. Jackson, Ken & Kreinin, Alexander & Zhang, Wanhe, 2009. "Randomization in the first hitting time problem," Statistics & Probability Letters, Elsevier, vol. 79(23), pages 2422-2428, December.
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