Evolutionary Statistical Procedures

Baragona, Roberto; Battaglia, Francesco; Poli, Irene

doi:10.1007/978-3-642-16218-3

Cited by 30 publications

(7 citation statements)

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“…This fact is particularly important for the numerical distributed models widely applied in geosciences [80] where no closed form is available for the posterior conditionals. However, the use of genetic operators as proposal functions that has given rise to the Evolutionary MCMC algorithms [81,82] is probably the most promising computational tool for the sampling unnormalized PDFs. The differential evolution adaptive metropolis (DREAM) Markov Chain Monte Carlo (MCMC) scheme [83,84] has been applied to hydrological sciences due to its better convergence and mixing properties [85].…”

Section: Markov Chain Monte Carlo Sampling Methodsmentioning

confidence: 99%

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

et al. 2011

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Parameter identification is one of the key elements in the construction of models in geosciences. However, inherent difficulties such as the instability of ill-posed problems or the presence of multiple local optima may impede the execution of this task. Regularization methods and Bayesian formulations, such as the maximum a posteriori estimation approach, have been used to overcome those complications. Nevertheless, in some instances, a more in-depth analysis of the inverse problem is advisable before obtaining estimates of the optimal parameters. The Markov Chain Monte Carlo (MCMC) methods used in Bayesian inference have been applied in the last 10 years in several fields of geosciences such as hydrology, geophysics or reservoir engineering. In the present paper, a compilation of basic tools for inference and a case study illustrating the practical application of them are given. Firstly, an introduction to the Bayesian approach to the inverse problem is provided together with the most common sampling algorithms with MCMC chains. Secondly, a series of estimators for quantities of interest, such as the marginal densities or the normalization constant of the posterior distribution of the parameters, are reviewed. Those reduce the computational cost significantly, us-

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Section: Markov Chain Monte Carlo Sampling Methodsmentioning

confidence: 99%

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

et al. 2011

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“…A new population is formed by selecting fitter individuals from the parent population and the children population. After several generations (iteration), the algorithm converges to the best individual, which hopefully represents a (globally) optimal solution to the problem (Baragona et al (2011) and Gen and Cheng (2000)). …”

Section: Evolutionary Feature Selectionmentioning

confidence: 99%

“…The SVM parameters are optimized by using an evolutionary algorithm, the so-called Genetic Algorithm (GA) introduced by Holland (1975). Some recent papers that deal with GA are Michalewicz (1996), Gen and Cheng (2000), Melanie (1999), Haupt and Haupt (2004), Sivanandam and Deepa (2008) and Baragona et al (2011).…”

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confidence: 99%

Support Vector Machines with Evolutionary Feature Selection for Default Prediction

Härdle

Prastyo²,

Hafner

2012

SSRN Journal

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“…A promising solution is combining metaheuristic algorithms and statistical models. This is a novel research area that addresses problems characterized by large design space, high-order interactions between variables, and, complex and non-linear experimental surfaces [15]. Some examples of these hybridized methods can be found in [16][17][18][19][20][21] approach is proposed in [21] where an optimization algorithm, called Co-Information Composite Likelihood (COIL), based on the evolutionary paradigm is introduced by coupling cross-entropy sampling [22] and composite likelihood principles [23] to design novel enzymes with improved functionalities.…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes ant colony optimization for designing high dimensional experiments

Borrotti

Minervini

Lucrezia

et al. 2016

Applied Soft Computing

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a b s t r a c tIn a large number of experimental problems, high dimensionality of the search area and economical constraints can severely limit the number of experimental points that can be tested. Within these constraints, classical optimization techniques perform poorly, in particular, when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from statistical modeling and bio-inspired algorithms to effectively explore a huge search space, sampling only a limited number of experimental points. To this purpose, we introduce a novel approach, combining ant colony optimization (ACO) and naïve Bayes classifier (NBC) that is, the naïve Bayes ant colony optimization (NACO) procedure. We compare NACO with other similar approaches developing a simulation study. We then derive the NACO procedure with the goal to design artificial enzymes with no sequence homology to the extant one. Our final aim is to mimic the natural fold of 200 amino acids 1AGY serine esterase from Fusarium solani.

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Evolutionary Statistical Procedures

Abstract: The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cited by 30 publications

References 0 publications

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

Support Vector Machines with Evolutionary Feature Selection for Default Prediction

Naïve Bayes ant colony optimization for designing high dimensional experiments

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