Extremal Optimization: Methods Derived from Co-Evolution

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We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ''breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance proves ... continued below

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Boettcher, S. & Percus, A.G. July 13, 1999.

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We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ''breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.

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Medium: P; Size: vp.

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OSTI as DE00760530

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  • Genetic and Evolutionary Computing Conference, Orlando, FL, 07/13/1999--07/17/1999

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  • Report No.: LA-UR-99-2670
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 760530
  • Archival Resource Key: ark:/67531/metadc722797

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  • July 13, 1999

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  • Sept. 29, 2015, 5:31 a.m.

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  • March 30, 2016, 6:46 p.m.

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Boettcher, S. & Percus, A.G. Extremal Optimization: Methods Derived from Co-Evolution, article, July 13, 1999; New Mexico. (digital.library.unt.edu/ark:/67531/metadc722797/: accessed April 25, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.