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Instance similarity and the effectiveness of case injection in a genetic algorithm for binary quadratic programming

Published: 08 July 2006 Publication History

Abstract

When an evolutionary algorithm addresses a sequence of instances of the same problem, it can seed its population with solutions that it found for previous instances. This technique is called case injection. How similar must the instances be for case injection to help an EA's search? We consider this question by applying a genetic algorithm, without and with case injection, to sequences of instances of binary quadratic programming. When the instances are similar, case injection helps; when the instances differ sufficiently, case injection is no help at all.

References

[1]
Jason Amunrud Bryant A. Julstrom St. Cloud State University, St. Cloud, MN
[2]
Sushil J. Louis and John McDonnell. Learning with case injected genetic algoritms. IEEE Transactions on Evolutionary Computation, 8(4):316--328, 2004.
[3]
S. Sahni. Computationally related problems. SIAM Journal on Computing, 3:262--279, 1974.
[4]
Wne-Jun Yin, Min Liu, and Cheng Wu. A genetic learning approach with case-based memory for job-shop scheduling problems. In Proceedings of the 2002 International Conference on Machine Learning and Cybernetics, volume--3, pages 1683--1687, 2002.

Cited By

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  • (2014)Learning robust build-orders from previous opponents with coevolution2014 IEEE Conference on Computational Intelligence and Games10.1109/CIG.2014.6932905(1-8)Online publication date: Aug-2014
  • (2013)Finding robust strategies to defeat specific opponents using case-injected coevolution2013 IEEE Conference on Computational Inteligence in Games (CIG)10.1109/CIG.2013.6633656(1-8)Online publication date: Aug-2013

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  1. Instance similarity and the effectiveness of case injection in a genetic algorithm for binary quadratic programming

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      cover image ACM Conferences
      GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation
      July 2006
      2004 pages
      ISBN:1595931864
      DOI:10.1145/1143997
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 08 July 2006

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      Author Tags

      1. binary quadratic programming
      2. case injection
      3. instance similarity
      4. population seeding

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      GECCO06
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      GECCO06: Genetic and Evolutionary Computation Conference
      July 8 - 12, 2006
      Washington, Seattle, USA

      Acceptance Rates

      GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;
      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      Cited By

      View all
      • (2014)Learning robust build-orders from previous opponents with coevolution2014 IEEE Conference on Computational Intelligence and Games10.1109/CIG.2014.6932905(1-8)Online publication date: Aug-2014
      • (2013)Finding robust strategies to defeat specific opponents using case-injected coevolution2013 IEEE Conference on Computational Inteligence in Games (CIG)10.1109/CIG.2013.6633656(1-8)Online publication date: Aug-2013

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