Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

By K. Behzadian, Zoran Kapelan, Dragan A. Savic, A. Ardeshir. 2009

147 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanical Engineering, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. K. Sudheer
2. Donald H. Burn
3. Wei Gong
4. Qingyun Duan
5. Zhenhua Di
6. Bryan Tolson
7. Saman Razavi
8. Aizhong Ye

In Mechanical Engineering

1. K. Sudheer
2. Donald H. Burn
3. Bryan Tolson
4. Saman Razavi
5. K. Kasiviswanathan

In Statistics and Probability

1. K. Sudheer
2. Donald H. Burn
3. Wei Gong
4. Chen Wang
5. Qingyun Duan
6. Zhenhua Di
7. Bryan Tolson
8. Saman Razavi

Citations in the corpus

• An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “…(Fen et al., 2009), regression tree method (Tree) (Breiman et al., 1984), Random Forest (RF) method (Breiman, 2001), MARS (Friedman, 1991), Support Vector Machines (SVMs) (Zhang et al., 2009), Artificial Neural Networks (ANNs) (Behzadian et al., 2009) and GP (Rasmussen andWilliams, 2006).”
• Review of surrogate modeling in water resources (2012, methodology): “Behzadian et al. [2009] and Broad et al. [2010] are the only studies reporting successful applications of metamodeling in relatively largesized problems (with 50 and 49 decision variables, respectively). Notably in both studies, the number of original model evaluations are very large ( 10,000), and ”
• Quantification of the predictive uncertainty of artificial neural network based river flow forecast models (2012, methodology): “The parameters of the ANN are generally obtained using an optimization algorithm, and genetic algorithm (Holland 1975; Goldberg 1989; Behzadian and Kapelan 2009) is employed in the current study.”
• Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, methodology): “…2001), smoothing splines ANOVA models (Gu, 2002; Ratto and Pagano, 2010; Storlie et al., 2011; Wahba, 1990), artificial neural networks (ANNs e Behzadian et al., 2009; Khu and Werner, 2003; Papadrakakis et al., 1998) and radial basis function (RBF) models (Hussain et al., 2002; Mugunthan et…”