A Convergence Analysis of Unconstrained and Bound Constrained Evolutionary Pattern Search

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The authors present and analyze a class of evolutionary algorithms for unconstrained and bound constrained optimization on R{sup n}: evolutionary pattern search algorithms (EPSAs). EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. They show that EPSAs can be cast as stochastic pattern search methods, and they use this observation to prove that EpSAs have a probabilistic weak stationary point convergence theory. This work provides the first convergence analysis for a class of evolutionary algorithms that guarantees ... continued below

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26 p.

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Hart, W.E. April 22, 1999.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM, and Livermore, CA (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

The authors present and analyze a class of evolutionary algorithms for unconstrained and bound constrained optimization on R{sup n}: evolutionary pattern search algorithms (EPSAs). EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. They show that EPSAs can be cast as stochastic pattern search methods, and they use this observation to prove that EpSAs have a probabilistic weak stationary point convergence theory. This work provides the first convergence analysis for a class of evolutionary algorithms that guarantees convergence almost surely to a stationary point of a nonconvex objective function.

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26 p.

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OSTI as DE00005908

Medium: P; Size: 26 pages

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  • Journal Name: Evolutionary Computation; Other Information: Submitted to Evolutionary Computation

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  • Report No.: SAND99-1010J
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 5908
  • Archival Resource Key: ark:/67531/metadc697290

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  • April 22, 1999

Added to The UNT Digital Library

  • Aug. 14, 2015, 8:43 a.m.

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  • April 10, 2017, 6:26 p.m.

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Hart, W.E. A Convergence Analysis of Unconstrained and Bound Constrained Evolutionary Pattern Search, article, April 22, 1999; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc697290/: accessed December 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.