Ensamblado de fragmentos de ADN utilizando un novedoso algoritmo de luciérnaga en GPU

Vidal, Pablo Javier; Olivera, Ana Carolina

doi:10.15446/dyna.v85n204.60078

Cited by 3 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The KPLS parameter selection algorithm (Algorithm 3) was run k = 30 times for each configuration [47], extracting the best results, mean and standard deviation of the estimates, and running time [48]. A non-parametric statistical analysis of the results obtained for the different MA configurations was carried out [49].…”

Section: Dataset and Pilot Frameworkmentioning

confidence: 99%

Improved Predictive Ability of KPLS Regression with Memetic Algorithms

2021

View full text Add to dashboard Cite

Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability.

show abstract

Section: Dataset and Pilot Frameworkmentioning

confidence: 99%

Improved Predictive Ability of KPLS Regression with Memetic Algorithms

2021

View full text Add to dashboard Cite

show abstract

KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

Mello-Román

Hernández

2020

IEEE Access

View full text Add to dashboard Cite

Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This paper proposes a methodology to simultaneously estimate both the parameters of the kernel function and the number of components of the KPLS regression to maximize its predictive ability. A metaheuristic optimization problem was proposed taking the cumulative cross-validation coefficient as an objective function to be maximized. It was solved using nature-inspired metaheuristic algorithms: the genetic algorithm, particle swarm optimization, grey wolf optimization and the firefly algorithm. To validate the results and have a reference measure of the efficiency of the natureinspired metaheuristic algorithms, derivative-free optimization algorithms were also applied: Hooke-Jeeves and Nelder-Mead. The metaheuristic algorithms estimated optimal values of both of the kernel function parameters and the number of components in the KPLS regression. INDEX TERMS Partial least squares regression, kernel-based method, cross-validation method, and nature-inspired metaheuristic algorithms.

show abstract

Análisis del Algoritmo Genético Celular para el Problema de Ensamblado de Cadenas de ADN

Pastrana¹,

Olivera²,

Vidal³

2019

ICTUNPA

View full text Add to dashboard Cite

n este trabajo, presentamos un análisis del comportamiento de un Algoritmo Genético Celular (cGA) aplicado al Problema de Ensamblado de cadenas de Ácido Desoxirribonucleico (ADN- FAP). Se construyeron 12 configuraciones para algoritmo basadas en dos operadores de mutación y dos tamaños de población. Luego de analizar los resultados obtenidos por las distintas configuraciones sobre instancias de la literatura se muestra claramente que los valores obtenidos considerando la utilización de operadores no adaptados al problema son promisorios a nivel de cubrimiento y alineamiento de cadenas de ADN.

show abstract

Ensamblado de fragmentos de ADN utilizando un novedoso algoritmo de luciérnaga en GPU

Cited by 3 publications

References 36 publications

Improved Predictive Ability of KPLS Regression with Memetic Algorithms

Improved Predictive Ability of KPLS Regression with Memetic Algorithms

KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

Análisis del Algoritmo Genético Celular para el Problema de Ensamblado de Cadenas de ADN

Contact Info

Product

Resources

About