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An analysis of control parameters of copula-based EDA algorithm with model migration

Published: 13 July 2019 Publication History

Abstract

The copula-based EDA algorithms nowadays represent a promising technique for problem optimization in the continuous domain. This paper provides a detailed analysis on how six key parameters of the parallel copula-based EDA with model migration (mCEDA) influence the quality of optimization. In order to improve the performance of that kind of algorithm the most suitable setting of these control parameters is evaluated on the well known CEC 2013 benchmark using inferential statistics.

References

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F. Caraffini, G. Iacca, F. Neri, L. Picinali, and E. Mininno. 2013. A CMA-ES super-fit scheme for the re-sampled inheritance search. In 2013 IEEE Congress on Evolutionary Computation. 1123--1130.
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Stephen Chen and Yasser Gonzalez-Fernandez. 2015. Leaders and Followers on the CEC2013 Real-Parameter Optimization Benchmark Functionss. Technical Report. Technical Report School of Information Technology York University.
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M. G. Epitropakis, F. Caraffini, F. Neri, and E. K. Burke. 2014. A Separability Prototype for Automatic Mernes with Adaptive Operator Selection. In Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on. 70--77.
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Martin Hyrš and Josef Schwarz. 2015. Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration. In Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015). SciTePress - Science and Technology Publications, 212--219.
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JJ Liang, BY Qu, PN Suganthan, and Alfredo G Hernández-Díaz. 2013. Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report 201212 (2013).
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Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Leslie Pérez Cáceres, Thomas Stützle, and Mauro Birattari. 2016. The irace package: Iterated Racing for Automatic Algorithm Configuration. Operations Research Perspectives 3 (2016), 43--58.
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I. Loshchilov. 2013. CMA-ES with restarts for solving CEC 2013 benchmark problems. In 2013 IEEE Congress on Evolutionary Computation. 369--376.
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Marta Soto, Yasser González-Fernández, and Alberto Ochoa. 2012. Modeling with Copulas and Vines in Estimation of Distribution Algorithms. CoRR abs/1210.5500 (2012).
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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Author Tags

  1. EDA
  2. copulas
  3. model migration
  4. optimization
  5. parallelization

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  • Research-article

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  • Vysoké Uðení Technické v Brnð

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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