• Corpus ID: 13830969

Modeling with Copulas and Vines in Estimation of Distribution Algorithms

@article{Soto2012ModelingWC,
  title={Modeling with Copulas and Vines in Estimation of Distribution Algorithms},
  author={Marta Soto and Yasser Gonz{\'a}lez-Fern{\'a}ndez and Carlos Alberto Ochoa Ort{\'i}z Zezzatti},
  journal={ArXiv},
  year={2012},
  volume={abs/1210.5500}
}
The aim of this work is studying the use of copulas and vines in numerical optimization with Estimation of Distribution Algorithms (EDAs). Two EDAs built around the multivariate product and normal copulas, and other two based on pair-copula decomposition of vine models are studied. We analyze empirically the effect of both marginal distributions and dependence structure in order to show that both aspects play a crucial role in the success of the optimization process. The results show that the… 
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14 Citations
Highly Influential Citations
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Background Citations
3
Methods Citations
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14 Citations

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An island-based version of copula-based EDA with probabilistic model migration (mCEDA) was tested on a set of well-known standard optimization benchmarks in the continuous domain and experimental results confirm that this concept of model migration yields better convergence as compared with the sequential version (sC EDA) and other recently published copulas-based EDAs.

Estimation of Distribution Algorithm Based on a Multivariate Extension of the Archimedean Copula

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Advanced parallel copula based EDA

  • Martin HyrsJ. Schwarz
  • Computer Science
    2016 IEEE Symposium Series on Computational Intelligence (SSCI)
  • 2016
This work investigated eight variants of island-based algorithms utilizing the capability of promising copula families, inter-island migration and additional adaptation of marginal parameters using CT-AVS technique to improve the efficiency of current copula based EDAs.

Vine Estimation of Distribution Algorithms

Four algorithms are investigated in relation to their ability to deal with both weak and strong correlated variables in continuous unconstrained optimization problems, and show that the models complement each other, although VEDA is the most promising algorithm.

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

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.

Vine copula-based EDA for dynamic multiobjective optimization

  • A. Cheriet
  • Computer Science
    Evolutionary Intelligence
  • 2020
Dynamic Multiobjective Problems cover a set of real-world problems that have many conflicting objectives. These problems are challenging and well known by the dynamic nature of their objective…

Multivariate Gaussian copula in Estimation of Distribution Algorithm with model migration

  • Martin HyrsJ. Schwarz
  • Computer Science
    2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)
  • 2014
The paper presents a new concept of an island-based model of Estimation of Distribution Algorithms (EDAs) with a bidirectional topology in the field of numerical optimization in continuous domain.…

Modeling dependencies between decision variables and objectives with copula models

This paper investigates the suitability of copula models to capture and exploit the dependencies between decision variables and random variables in MOPs with a continuous representation.

Modelling the Dependence Structure of Financial Assets: A Bivariate Extreme Data Study

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