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From Genetic Variation to Probabilistic Modeling
Genetic algorithms ⦓GAs) [53, 83] are stochastic optimization methods inspired by natural evolution and genetics. Over the last few decades, GAs have...
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Hierarchical Bayesian Optimization Algorithm
The previous chapter has discussed how hierarchy can be used to reduce problem complexity in black-box optimization. Additionally, the chapter has...
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The Challenge of Hierarchical Difficulty
Thus far, we have examined the Bayesian optimization algorithm (BOA), empirical results of its application to several problems of bounded difficulty,...
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Hierarchical BOA in the Real World
The last chapter designed hBOA, which was shown to provide scalable solution for hierarchical traps. Since hierarchical traps were designed to test...
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Bayesian Optimization Algorithm
The previous chapter argued that using probabilistic models with multivariate interactions is a powerful approach to solving problems of bounded...
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Probabilistic Model-Building Genetic Algorithms
The previous chapter showed that variation operators in genetic and evolutionary algorithms can be replaced by learning a probabilistic model of...
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Scalability Analysis
The empirical results of the last chapter were tantalizing. Easy and hard problems were automatically solved without user intervention in polynomial...
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Summary and Conclusions
The purpose of this chapter is to provide a summary of main contributions of this work and outline important conclusions.
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A Mass-Conservative Reduced-Order Algorithm in Solving Optimal Control of Convection-Diffusion Equation
This paper introduces a novel approach, the mass-conservative reduced-order characteristic finite element (MCROCFE) method, designed for optimal...
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An innovative fourth-order numerical scheme with error analysis for Lane-Emden-Fowler type systems
In this paper, we develop a novel higher-order compact finite difference scheme for solving systems of Lane-Emden-Fowler type equations. Our method...
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Enhancing SNV identification in whole-genome sequencing data through the incorporation of known genetic variants into the minimap2 index
MotivationAlignment of reads to a reference genome sequence is one of the key steps in the analysis of human whole-genome sequencing data obtained...
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Sublinear Algorithms in T-Interval Dynamic Networks
We consider standard T - interval dynamic networks , under the synchronous timing model and the broadcast CONGEST model. In a T - interval dynamic network ,...
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METASEED: a novel approach to full-length 16S rRNA gene reconstruction from short read data
BackgroundWith the emergence of Oxford Nanopore technology, now the on-site sequencing of 16S rRNA from environments is available. Due to the error...
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expHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer
BackgroundHomologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy...
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Superconvergence Analysis of a Robust Orthogonal Gauss Collocation Method for 2D Fourth-Order Subdiffusion Equations
In this paper, we study the orthogonal Gauss collocation method (OGCM) with an arbitrary polynomial degree for the numerical solution of a...
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Unconditional energy stability and maximum principle preserving scheme for the Allen-Cahn equation
In this paper, we propose a novel fully implicit numerical scheme that satisfies both nonlinear energy stability and maximum principle for the space...
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Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database
BackgroundThe use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of...
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Gradient-Based Monte Carlo Methods for Relaxation Approximations of Hyperbolic Conservation Laws
Particle methods based on evolving the spatial derivatives of the solution were originally introduced to simulate reaction-diffusion processes,...
