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Variance reduction in meta-EDA

Published: 07 July 2007 Publication History

Abstract

We study the benefit of measurement replication when using the Relevance Estimation and Value Calibration method to calibrate a genetic algorithm. We find that replication is not essential to REVAC, which makes it a strong alternative to existing statistical tools which are computationally costly.

References

[1]
A. Czarn, C. MacNish, K. Vijayan, B. Turlach, and R. Gupta. Statistical Exploratory Analysis of Genetic Algorithms. IEEE Transactions on Evolutionary Computation, 8(4):405--421, 2004.
[2]
H. Mühlenbein. The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3):303--346, 1997.
[3]
V. Nannen and A. E. Eiben. Relevance Estimation and Value Calibration of Evolutionary Algorithm Parameters. In M. M. Veloso et al., editors, Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI'07, pages 1034--1039. AAAI Press, Jan. 2007.

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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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  1. estimation of distribution algorithms
  2. parameter control

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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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