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Parameter cross-validation and early-stopping in univariate marginal distribution algorithm

Published: 07 July 2007 Publication History

Abstract

In this paper, a cross-validation and early-stopping algorithm is devised for parameter updating in the Univariate Marginal Distribution Algorithm (UMDA) to reduce overftting. Our hypothesis is that the well-known problem of diversity loss in UMDA is a consequence of overfitting during the parameter estimation step at each generation. It is tested by experiments on two different optimization problems.

References

[1]
Pedro Larranaga, Jose A. Lozano. Estimation of Distribution Algorithms. University of the Basque Country, Spain, 2002.
[2]
J. L Shapiro, Diversity loss in general estimation of distribution algorithms. In Parallel Problem Solving from Nature, volume 4193 of LNCS, pages 92--101. 2006.
[3]
Hao Wu and J.L. Shapiro, Does overfitting Affect performance in Estimation of Distribution Algorithms. GECCO'06, Seattle, USA 2006

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Published In

cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

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Author Tags

  1. cross-validation
  2. early-stopping
  3. overfitting
  4. univariate marginal distribution algorithm (UMDA)

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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