Correlation control in small-sample Monte Carlo type simulations I: A simulated annealing approach

Vořechovský, Miroslav; Novák, Drahomı́r

doi:10.1016/j.probengmech.2009.01.004

Cited by 178 publications

(89 citation statements)

References 36 publications

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“…Moreover, there was no need to bother with dimensionality, and the number of samples can be any integer [13]. LSH was widely used to improve the algorithm efficiency, such as Cholesky decomposition [12], single switch optimization algorithm [13], Columnwise-pairwise algorithm [14], Rank GramSchmidt algorithm [15] and simulated annealing algorithm [16].…”

Section: Lhs and Its Properties 21 Lhsmentioning

confidence: 99%

Regression modeling based on improved genetic algorithm

Shi

Benda

et al. 2017

Teh. vjesn.

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Original scientific paper Regression model is a well-established method in data analysis with applications in various fields. The selection of independent variables and mathematically transformed in a regression model is often a challenging problem. Recently, some scholars have used evolutionary computation to solve this problem, but the result is not effective as we desired. The crossover operation in GA is redesigned by using Latin hypercube sampling, then combining two commonly used statistical criteria (AIC, BIC) we are presenting an improved genetic algorithm based for solving statistical model selection problem. The proposed algorithm can overcome strong path-dependence and rely on experience of classical approaches. Comparison of simulation results in solving statistical model selection problem with this improved GA, traditional genetic algorithm and classical algorithm for model selection show that the new GA has superiority in solution of quality, convergence rate and other various indices. Keywords: genetic algorithm; Latin hypercube sampling; regression analysis; regression model selection Regresijsko modeliranje zasnovano na poboljšanom genetičkom algoritmuIzvorni znanstveni članak Regresijski model je dobro uhodana metoda u analizi podataka s primjenom u raznim područjima. Izbor nezavisnih varijabli i matematički transformiranih u regresijski model, često predstavlja izazovan problem. Nedavno je nekoliko znanstvenika primijenilo evolucijski proračun za rješenje tog problema, ali rezultat nije učinkovit onoliko koliko smo željeli. Ukrižena (crossover) operacija u GA redizajnirana je primjenom Latin hypercube uzorkovanja, a zatim, kombinacijom dvaju uobičajeno korištenih statističkih kriterija (AIC, BIC), dajemo poboljšani genetički algoritam za rješavanje problema izbora statističkog modela. Predloženim se algoritmom može prevladati jaka ovisnost o putanji i osloniti na iskustvo stečeno primjenom klasičnih pristupa. Usporedba rezultata simulacije u rješavanju problema odabira statističkog modela s ovim poboljšanim GA, tradicionalnog genetičkog algoritma i klasičnog algoritma za odabir modela pokazuje da je novi GA superiorniji u rješavanju kvalitete, brzine konvergencije i drugih različitih pokazatelja.

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Section: Lhs and Its Properties 21 Lhsmentioning

confidence: 99%

Regression modeling based on improved genetic algorithm

Shi

Benda

et al. 2017

Teh. vjesn.

View full text Add to dashboard Cite

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“…Firstly, during sampling, an undesired correlation can be introduced between random variables, and secondly, how to introduce prescribed statistical correlations between pairs of variables [44].…”

Section: Correlation Controlmentioning

confidence: 99%

“…Issue 2 arises when there are known correlations between variables in the analysis that cannot be ignored. The question is how the sample can be rearranged in such a way as to fulfil the two requirements of removing undesired correlation, but at the same time introducing prescribed correlations [44].…”

Section: Correlation Controlmentioning

confidence: 99%

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Probabilistic analysis of a coal mine roadway including correlation control between model input parameters

Booth

Marshall

Stace

2016

Computers and Geotechnics

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Abstract:Probabilistic analysis and numerical modelling techniques have been combined to analyse a deep coal mine roadway. Using the Monte Carlo method, a correlation control algorithm and a FLAC 2D finite difference model, a probability distribution of roof displacements has been calculated and compared to a set of measurements from an actual mine roadway. The importance of correlation between input parameters is also considered. The results show that the analysis performs relatively well, but does tend to over predict the magnitude of displacements. Correlation between parameters is shown to be very important, particularly between the three model stresses.

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“…The advanced Monte Carlo method was applied (Vořechovský, Novák 2009;Vořechovský 2012). The case with statistically independent input random variables X i was studied.…”

Section: Sobol Sensitivity Analysismentioning

confidence: 99%

Sensitivity Analysis of Tangential Digging Forces of the Bucket Wheel Excavator SCHRS 1320 for Different Terraces

Gottvald

Kala

2012

Journal of Civil Engineering and Management

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Abstract. Researches in the field of economy of control of mining processes of minerals are continually met with a number of unsolved and newly arising problems. New deposits of brown coal are located at greater depths than before and are more difficult to mine. Problems in the field of rock mining stem from the greater diversity of rocks in mining, and it results in more difficult mining conditions. On the other hand, even greater demands are made to decrease the economic demand of mining. Current research is aimed at the most optimal utilization of energy provided on the bucket wheel of excavators, such that the energy demand of the mining process decreases, whilst preserving maximum performance. These requirements place greater demands on the precision of planning of mining and maximum economic efficiency. The presented article illustrates the possibilities of the utilization of Sobol sensitivity analysis during the investigation of the influence of the parameters of mining processes on tangential digging forces. Analyses are carried out for the mining process of bucket wheel excavator SchRs 1320. For purposes of the study, detailed measurements of operational parameters were performed on the excavator during a whole work cycle.

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Correlation control in small-sample Monte Carlo type simulations I: A simulated annealing approach

Cited by 178 publications

References 36 publications

Regression modeling based on improved genetic algorithm

Regression modeling based on improved genetic algorithm

Probabilistic analysis of a coal mine roadway including correlation control between model input parameters

Sensitivity Analysis of Tangential Digging Forces of the Bucket Wheel Excavator SCHRS 1320 for Different Terraces

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