Global Optimization with the Gaussian Polytree EDA

@inproceedings{SegoviaDominguez2011GlobalOW,
  title={Global Optimization with the Gaussian Polytree EDA},
  author={Ignacio Segovia-Dominguez and Arturo Hern{\'a}ndez Aguirre and Enrique Ra{\'u}l Villa Diharce},
  booktitle={MICAI},
  year={2011}
}
This paper introduces the Gaussian polytree estimation of distribution algorithm, a new construction method, and its application to estimation of distribution algorithms in continuous variables. The variables are assumed to be Gaussian. The construction of the tree and the edges orientation algorithm are based on information theoretic concepts such as mutual information and conditional mutual information. The proposed Gaussian polytree estimation of distribution algorithm is applied to a set of… 

Building multivariate density functions based on promising direction vectors

TLDR
This paper introduces a method to build a large variety of multivariate density functions based on univariate distributions and promising direction vectors and provides two algorithms to use this ideas in the global optimization problem.

References

SHOWING 1-10 OF 18 REFERENCES

The gaussian polytree EDA for global optimization

This paper explains how to construct Gaussian polytrees and their application to estimation of distribution algorithms in continuous variables.

Approximating discrete probability distributions with dependence trees

TLDR
It is shown that the procedure derived in this paper yields an approximation of a minimum difference in information when applied to empirical observations from an unknown distribution of tree dependence, and the procedure is the maximum-likelihood estimate of the distribution.

Learning Polytrees

TLDR
A performance guarantee is established that the optimal branching (or Chow-Liu tree), which can be computed very easily, constitutes a good approximation to the best polytree.

Learning in belief networks and its application to distributed databases

TLDR
This thesis first considers methods to build tree structures and use these trees as a basis to build a richer structure, namely a polytree graph, and presents formal techniques to generate random distributions obeying polytree dependence models.

Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift

TLDR
This work focuses on a second source of inefficiency that is not removed by existing remedies and provides a simple, but effective technique called Anticipated Mean Shift (AMS) that removes this inefficiency.

The recovery of causal poly-trees from statistical data

Learning Bayesian networks

TLDR
This chapter discusses Bayesian Networks, a framework for Bayesian Structure Learning, and some of the algorithms used in this framework.

The correlation-triggered adaptive variance scaling IDEA

TLDR
The CT-AVS-IDEA is compared to the original IDEA and the Evolution Strategy with Covariance Matrix Adaptation on a wide range of unimodal test-problems by means of a scalability analysis and is found to enlarge the class of problems that continuous EDAs can solve reliably.

Modeling and Reasoning with Bayesian Networks

TLDR
This book provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.