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18
Parameterless hierarchical BOA
 Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2004), Part II, LNCS 3103
, 2004
"... An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distancebased statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous ..."
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An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distancebased statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous hBOA runs and using the obtained statistics to bias future hBOA runs on similar problems. The purpose of this paper is threefold: (1) test the technique on several classes of NPcomplete problems, including MAXSAT, spin glasses and minimum vertex cover; (2) demonstrate that the technique is effective even when previous runs were done on problems of different size; (3) provide empirical evidence that combining transfer learning with other efficiency enhancement techniques can often provide nearly multiplicative speedups.
M.: Distancebased bias in modeldirected optimization of additively decomposable problems
, 2012
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copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas
, 1209
"... The use of probabilistic models based on copulas in EDAs (Estimation of Distribution Algorithms)iscurrentlyanactiveareaofresearch. Inthiscontext, the copulaedaspackage for R intends to provide a platform where EDAs based on copulas can be implemented and studied. The package offers complete implemen ..."
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The use of probabilistic models based on copulas in EDAs (Estimation of Distribution Algorithms)iscurrentlyanactiveareaofresearch. Inthiscontext, the copulaedaspackage for R intends to provide a platform where EDAs based on copulas can be implemented and studied. The package offers complete implementations of various EDAs based on copulas and vines, a group of wellknown benchmark problems, and utility functions to study the behavior of EDAs. It is also possible to implement new algorithms that can be easily integrated into the package, since EDAs are defined using S4 classes with genericfunctionsforitsmaincomponents. Thispaperpresentscopulaedasbyprovidingan overview of EDAs based on copulas, a description of the implementation of the package, and an illustration of its use with examples. The examples include running the EDAs implemented in the package, implementing new algorithms, and performing an empirical study to compare the behavior of a group of algorithms on benchmark functions and a realworld problem.
Transfer learning, soft distancebased bias, and the hierarchical boa
, 2012
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Learn from the Past: Improving ModelDirected . . . Distancebased Bias
, 2012
"... For many optimization problems it is possible to define a problemspecific distance metric over decision variables that correlates with the strength of interactions between the variables. Examples of such problems include additively decomposable functions, facility location problems, and atomic clus ..."
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For many optimization problems it is possible to define a problemspecific distance metric over decision variables that correlates with the strength of interactions between the variables. Examples of such problems include additively decomposable functions, facility location problems, and atomic cluster optimization. However, the use of such a metric for enhancing efficiency of optimization techniques is often not straightforward. This paper describes a framework that allows optimization practitioners to improve efficiency of modeldirected optimization techniques by combining such a distance metric with information mined from previous optimization runs on similar problems. The framework is demonstrated and empirically evaluated in the context of the hierarchical Bayesian optimization algorithm (hBOA). Experimental results provide strong empirical evidence that the proposed approach provides significant speedups and that it can be effectively combined with other efficiency enhancements. The paper demonstrates how straightforward it is to adapt the proposed framework to other modeldirected optimization techniques by presenting several examples.
Structural coherence of problem and algorithm: An analysis for EDAs on all 2bit and 3bit problems
 In Proc. IEEE CEC
, 2015
"... Abstract—Metaheuristics assume some kind of coherence between decision and objective spaces. Estimation of Distribution algorithms approach this by constructing an explicit probabilistic model of high fitness solutions, the structure of which is intended to reflect the structure of the problem. In ..."
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Abstract—Metaheuristics assume some kind of coherence between decision and objective spaces. Estimation of Distribution algorithms approach this by constructing an explicit probabilistic model of high fitness solutions, the structure of which is intended to reflect the structure of the problem. In this context, “structure ” means the dependencies or interactions between problem variables in a probabilistic graphical model. There are many approaches to discovering these dependencies, and existing work has already shown that often these approaches discover “unnecessary ” elements of structure that is, elements which are not needed to correctly rank solutions. This work performs an exhaustive analysis of all 2 and 3 bit problems, grouped into classes based on mononotic invariance. It is shown in [1] that each class has a minimal Walsh structure that can be used to solve the problem. We compare the structure discovered by different structure learning approaches to the minimal Walsh structure for each class, with summaries of which interactions are (in)correctly identified. Our analysis reveals a large number of symmetries that may be used to simplify problem solving. We show that negative selection can result in improved coherence between discovered and necessary structure, and conclude with some directions for a general programme of study building on this work. I.
Two approaches of using heavy tails in high dimensional EDA
"... AbstractWe consider the problem of high dimensional blackbox optimisation via Estimation of Distribution Algorithms (EDA). The Gaussian distribution is commonly used as a search operator in most of the EDA methods. However there are indications in the literature that heavy tailed distributions ma ..."
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AbstractWe consider the problem of high dimensional blackbox optimisation via Estimation of Distribution Algorithms (EDA). The Gaussian distribution is commonly used as a search operator in most of the EDA methods. However there are indications in the literature that heavy tailed distributions may perform better due to their higher exploration capabilities. Univariate heavy tailed distributions were already proposed for high dimensional problems. In 2D problems it has been reported that a multivariate heavy tailed (such as Cauchy) search distribution is able to blend together the strengths of multivariate modelling with a high exploration power. In this paper, we study whether a similar scheme would work well in high dimensional search problems. To get around of the difficulty of multivariate model building in high dimensions we employ a recently proposed random projections (RP) ensemble based approach which we modify to get samples from a multivariate Cauchy using the scalemixture representation of the Cauchy distribution. Our experiments show that the resulting RPbased multivariate Cauchy EDA consistently improves on the performance of the univariate Cauchy search distribution. However, intriguingly, the RPbased multivariate Gaussian EDA has the best performance among these methods. It appears that the highly explorative nature of the multivariate Cauchy sampling is exacerbated in high dimensional search spaces and the population based search loses its focus and effectiveness as a result. Finally, we present an idea to increase exploration while maintaining exploitation and focus by using the RPbased multivariate Gaussian EDA in which the RP matrices are drawn with i.i.d. heavy tailed entries. This achieves improved performance and is competitive with the state of the art.
Combinatorial Optimization EDA using Hidden Markov Models
"... Estimation of Distribution Algorithms (EDAs) have been successfully applied to a wide variety of problems. The algorithmic model of EDA is generic and can virtually be used with any distribution model, ranging from the mere Bernoulli distribution to the sophisticated Bayesian network. The Hidden Mar ..."
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Estimation of Distribution Algorithms (EDAs) have been successfully applied to a wide variety of problems. The algorithmic model of EDA is generic and can virtually be used with any distribution model, ranging from the mere Bernoulli distribution to the sophisticated Bayesian network. The Hidden Markov Model (HMM) is a wellknown graphical model useful for modelling populations of variablelength sequences of discrete values. Surprisingly, HMMs have not yet been used as distribution estimators for an EDA, although they are a very powerful tool for estimating sequential samples. This paper thus proposes a new method, called HMMEDA, implementing this idea, along with some preliminary experimental results.