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Expected improvement in efficient global optimization through bootstrapped kriging

Kleijnen, Jack; Beers, Wim; Nieuwenhuyse, Inneke - In: Journal of Global Optimization 54 (2012) 1, pp. 59-73

This article uses a sequentialized experimental design to select simulation input combinations for global optimization … “efficient global optimization” (EGO) through the introduction of an improved estimator of the Kriging predictor variance; this …

Persistent link: https://www.econbiz.de/10010994014

Solving dual problems using a coevolutionary optimization algorithm

Deb, Kalyanmoy; Gupta, Shivam; Dutta, Joydeep; Ranjan, … - In: Journal of Global Optimization 57 (2013) 3, pp. 891-933

In solving certain optimization problems, the corresponding Lagrangian dual problem is often solved simply because in …. Another interesting aspect of dual problems is that both lower and upper-level optimization problems involve only box … constraints and no other equality of inequality constraints. In this paper, we propose a coevolutionary dual optimization (CEDO …

Persistent link: https://www.econbiz.de/10010994031

On the stability of the linear functional equation in a single variable on complete metric groups

Jung, Soon-Mo; Popa, Dorian; Rassias, Michael - In: Journal of Global Optimization 59 (2014) 1, pp. 165-171

In this paper we obtain a result on Hyers–Ulam stability of the linear functional equation in a single variable <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$f(\varphi (x))=g(x) \cdot f(x)$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mi>f</mi> <mo stretchy="false">(</mo> <mi mathvariant="italic">φ</mi> <mo stretchy="false">(</mo> <mi>x</mi> <mo stretchy="false">)</mo> <mo stretchy="false">)</mo> <mo>=</mo> <mi>g</mi> <mo stretchy="false">(</mo> <mi>x</mi> <mo stretchy="false">)</mo> <mo>·</mo> <mi>f</mi> <mo stretchy="false">(</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </math> </EquationSource> </InlineEquation> on a complete metric group. Copyright Springer Science+Business Media New York 2014

Persistent link: https://www.econbiz.de/10010994097

Improving differential evolution through a unified approach

Padhye, Nikhil; Bhardawaj, Piyush; Deb, Kalyanmoy - In: Journal of Global Optimization 55 (2013) 4, pp. 771-799

describe traditional forms of Evolutionary Optimization Systems such as Genetic Algorithms, Evolutionary Strategies …, Evolutionary Programming, Particle Swarm Optimization and differential evolution (DE). Then, a relatively new evolutionary paradigm …

Persistent link: https://www.econbiz.de/10010994117

Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance

Koc, Elcin; Iyigun, Cem; Batmaz, İnci; Weber, … - In: Journal of Global Optimization 60 (2014) 1, pp. 103-120

Multivariate adaptive regression splines (MARS) has become a popular data mining (DM) tool due to its flexible model building strategy for high dimensional data. Compared to well-known others, it performs better in many areas such as finance, informatics, technology and science. Many studies...

Persistent link: https://www.econbiz.de/10010994193

Incremental learning optimization on knowledge discovery in dynamic business intelligent systems

Liu, Dun; Li, Tianrui; Ruan, Da; Zhang, Junbo - In: Journal of Global Optimization 51 (2011) 2, pp. 325-344

Persistent link: https://www.econbiz.de/10009324646

Optimization algorithm for learning consistent belief rule-base from examples

Liu, Jun; Martinez, Luis; Ruan, Da; Rodriguez, Rosa; … - In: Journal of Global Optimization 51 (2011) 2, pp. 255-270

Persistent link: https://www.econbiz.de/10009324654

An improved training algorithm for feedforward neural network learning based on terminal attractors

Yu, Xinghuo; Wang, Bin; Batbayar, Batsukh; Wang, Liuping; … - In: Journal of Global Optimization 51 (2011) 2, pp. 271-284

Persistent link: https://www.econbiz.de/10009324658

Particle swarm optimization for bi-level pricing problems in supply chains

Gao, Ya; Zhang, Guangquan; Lu, Jie; Wee, Hui-Ming - In: Journal of Global Optimization 51 (2011) 2, pp. 245-254

Persistent link: https://www.econbiz.de/10009324662

Maturity, distance and density (MD<Superscript>2</Superscript>) metrics for optimizing trust prediction for business intelligence

Raza, Muhammad; Hussain, Omar; Hussain, Farookh; Chang, … - In: Journal of Global Optimization 51 (2011) 2, pp. 285-300

Persistent link: https://www.econbiz.de/10009324663