On Robustness of EDA Tools

In real-valued estimation-of-distribution algorithms, the Gaussian distribution is often used along with maximum likelihood (ML) estimation of its parameters. Such a process is highly prone to premature convergence. The simplest method for preventing premature convergence of gaussian distribution is to enlarge the maximum likelihood estimate of standard deviation σ by a constant factor k each generation. This paper surveys and broadens the theoretical models of the behaviour of this simple EDA on 1D problems and derives the limits for the constant k. The behaviour of this simple EDA with various values of k is analysed and the agreement of the model with the reality is confirmed.

2016 IEEE Congress on Evolutionary Computation (CEC), 2016

Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

2015 IEEE Congress on Evolutionary Computation (CEC), 2015

In this paper, we present a new variant of EDA for high dimensional continuous optimisation, which extends a recently proposed random projections (RP) ensemble based approach by employing heavy tailed random matrices. In particular, we use random matrices with i.i.d. t-distributed entries. The use of t-distributions may look surprising in the context of random projections, however we show that the resulting ensemble covariance is enlarged when the degree of freedom parameter is lowered. Based on this observation, we develop an adaptive scheme to adjust this parameter during evolution, and this results in a flexible means of balancing exploration and exploitation of the search process. A comprehensive set of experiments on high dimensional benchmark functions demonstrate the usefulness of our approach.

2009

This paper describes MATEDA-2.0, a suite of programs in Matlab for estimation of distribution algorithms. The package allows the optimization of single and multi-objective problems with estimation of distribution algorithms (EDAs) based on undirected graphical models and Bayesian networks. The implementation is conceived for allowing the incorporation by the user of different combinations of selection, learning, sampling, and local search procedures. Other included methods allow the analysis of the structures learned by the probabilistic models, the visualization of particular features of these structures and the use of the probabilistic models as fitness modeling tools.

Adaptive and Natural Computing Algorithms, 2005

This paper presents an extension to our work on estimating the probability distribution by using a Markov Random Field (MRF) model in an Estimation of Distribution Algorithm (EDA) [1]. We propose a method that directly samples a MRF model to generate new population. We also present a new EDA, called the Distribution Estimation Using MRF with direct sampling (DEUM d ), that uses this method, and iteratively refines the probability distribution to generate better solutions. Our experiments show that the direct sampling of a MRF model as estimation of distribution provides a significant advantage over other techniques on problems where a univariate EDA is typically used.

Lecture Notes in Computer Science, 2008

In real-valued estimation-of-distribution algorithms, the Gaussian distribution is often used along with maximum likelihood (ML) estimation of its parameters. Such a process is highly prone to premature convergence. The simplest method for preventing premature convergence of Gaussian distribution is enlarging the maximum likelihood estimate of σ by a constant factor k each generation. Such a factor should be large enough to prevent convergence on slopes of the fitness function, but should not be too large to allow the algorithm converge in the neighborhood of the optimum. Previous work showed that for truncation selection such admissible k exists in 1D case. In this article it is shown experimentaly, that for the Gaussian EDA with truncation selection in high-dimensional spaces no admissible k exists!

IEEE Transactions on Medical Imaging, 2007

Recently, advances have been made in continuous, normal- distribution-based Estimation-of-Distribution Algorithms (EDAs) by scaling the variance up from the maximum-like- lihood estimate. When done properly, such scaling has been shown to prevent premature convergence on slope-like re- gions of the search space. In this paper we specifically fo- cus on one way of scaling that was previously introduced as Adaptive

Lecture Notes in Computer Science, 2016

Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

2012

Many EDA development groups rely on full tool testing as the only means of determining program correctness. Meanwhile, other software development communities have made use of extensive unit tests as a method of assuring high-quality components. We believe that the value of such testing is derived from principles familiar to the EDA community from hardware test generation: the need for both controllability and observability of a unit under test to verify correct behavior. We believe that unit testing has value both for the development of new code and for the understanding and adoption of existing code, though it may require substantial refactoring to make older code testable. We explore both the value and costs of unit testing for EDA software, as well as noting some alternatives to stand-alone unit testing that may provide some degree of observability and controllability when full unit testing is impractical. We describe tools and resources we have used in putting together a unit te...

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008

In order to comprehend the advantages and shortcomings of each model-building algorithm they should be tested under similar conditions and isolated from the MOEDA it takes part of. In this work we will assess some of the main machine learning algorithms used or suitable for model-building in a controlled environment and under equal conditions. They are analyzed in terms of solution accuracy and computational complexity. To the best of our knowledge a study like this has not been put forward before and it is essential for the understanding of the nature of the model-building problem of MOEDAs and how they should be improved to achieve a quantum leap in their problem solving capacity.