Evolutionary pattern search algorithms

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This paper defines a class of evolutionary algorithms called evolutionary pattern search algorithms (EPSAs) and analyzes their convergence properties. This class of algorithms is closely related to evolutionary programming, evolutionary strategie and real-coded genetic algorithms. EPSAs are self-adapting systems that modify the step size of the mutation operator in response to the success of previous optimization steps. The rule used to adapt the step size can be used to provide a stationary point convergence theory for EPSAs on any continuous function. This convergence theory is based on an extension of the convergence theory for generalized pattern search methods. An experimental ... continued below

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26 p.

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Hart, W.E. September 19, 1995.

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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. It has been viewed 16 times . More information about this article can be viewed below.

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

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Description

This paper defines a class of evolutionary algorithms called evolutionary pattern search algorithms (EPSAs) and analyzes their convergence properties. This class of algorithms is closely related to evolutionary programming, evolutionary strategie and real-coded genetic algorithms. EPSAs are self-adapting systems that modify the step size of the mutation operator in response to the success of previous optimization steps. The rule used to adapt the step size can be used to provide a stationary point convergence theory for EPSAs on any continuous function. This convergence theory is based on an extension of the convergence theory for generalized pattern search methods. An experimental analysis of the performance of EPSAs demonstrates that these algorithms can perform a level of global search that is comparable to that of canonical EAs. We also describe a stopping rule for EPSAs, which reliably terminated near stationary points in our experiments. This is the first stopping rule for any class of EAs that can terminate at a given distance from stationary points.

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26 p.

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OSTI as DE96001609

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  • Foundations of genetic algorithms, San Diego, CA (United States), 3-6 Aug 1996

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  • Other: DE96001609
  • Report No.: SAND--95-2293C
  • Report No.: CONF-960820--1
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 244348
  • Archival Resource Key: ark:/67531/metadc672885

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  • September 19, 1995

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  • June 29, 2015, 9:42 p.m.

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  • April 14, 2016, 3:51 p.m.

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Hart, W.E. Evolutionary pattern search algorithms, article, September 19, 1995; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc672885/: accessed September 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.