25 research outputs found
Stationary probability density of stochastic search processes in global optimization
A method for the construction of approximate analytical expressions for the
stationary marginal densities of general stochastic search processes is
proposed. By the marginal densities, regions of the search space that with high
probability contain the global optima can be readily defined. The density
estimation procedure involves a controlled number of linear operations, with a
computational cost per iteration that grows linearly with problem size
Hierarchical adaptive polynomial chaos expansions
Polynomial chaos expansions (PCE) are widely used in the framework of
uncertainty quantification. However, when dealing with high dimensional complex
problems, challenging issues need to be faced. For instance, high-order
polynomials may be required, which leads to a large polynomial basis whereas
usually only a few of the basis functions are in fact significant. Taking into
account the sparse structure of the model, advanced techniques such as sparse
PCE (SPCE), have been recently proposed to alleviate the computational issue.
In this paper, we propose a novel approach to SPCE, which allows one to exploit
the model's hierarchical structure. The proposed approach is based on the
adaptive enrichment of the polynomial basis using the so-called principle of
heredity. As a result, one can reduce the computational burden related to a
large pre-defined candidate set while obtaining higher accuracy with the same
computational budget
Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization
This work is supported by the National Natural Science Foundation of China (Grant Nos. 61472159, 61572227), Development Project of Jilin Province of China (Nos. 20160204022GX, 20160414009GH, 2017C033).Postprin
Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures
This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin–Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures
A phenotypic analysis of three population-based metaheuristics
Metaheuristics are used as very good optimization methods and they imitate natural, biologic, social and cultural process. In this work, we evaluate and compare three different metaheuristics which are population-based: Genetic Algorithms, CHC and Scatter Search. They work with a set of solutions in contrast to trajectory-based metaheuristics which use an only solution. From a comparative analysis, we can infer that Genetic Algorithms and CHC algorithms can solve satisfactorily problems with a growing complexity. While Scatter Search provides high quality solutions but its computational effort is very high too.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
Estimation of constant and time-varying dynamic parameters of HIV infection in a nonlinear differential equation model
Modeling viral dynamics in HIV/AIDS studies has resulted in a deep
understanding of pathogenesis of HIV infection from which novel antiviral
treatment guidance and strategies have been derived. Viral dynamics models
based on nonlinear differential equations have been proposed and well developed
over the past few decades. However, it is quite challenging to use experimental
or clinical data to estimate the unknown parameters (both constant and
time-varying parameters) in complex nonlinear differential equation models.
Therefore, investigators usually fix some parameter values, from the literature
or by experience, to obtain only parameter estimates of interest from clinical
or experimental data. However, when such prior information is not available, it
is desirable to determine all the parameter estimates from data. In this paper
we intend to combine the newly developed approaches, a multi-stage
smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares
(SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear
differential equation model. In particular, to the best of our knowledge, this
is the first attempt to propose a comparatively thorough procedure, accounting
for both efficiency and accuracy, to rigorously estimate all key kinetic
parameters in a nonlinear differential equation model of HIV dynamics from
clinical data. These parameters include the proliferation rate and death rate
of uninfected HIV-targeted cells, the average number of virions produced by an
infected cell, and the infection rate which is related to the antiviral
treatment effect and is time-varying. To validate the estimation methods, we
verified the identifiability of the HIV viral dynamic model and performed
simulation studies.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS290 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org