Formulation of the Optimal Latin Hypercube Design of Experiments Using a Permutation Genetic Algorithm

By S. Bates, J. Sienz, Vassili Toropov. 2004

158 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/41b5ea5eaa6d99db21802e577e485dd2ee9e4163

How researchers see this paper

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

In Computational Theory and Mathematics

1. Felipe Antonio Chegury Viana
2. Claudia D'Ambrosio
3. Gerhard Venter
4. Vladimir Balabanov
5. Youssef Hamadi
6. Leo Liberti
7. Ky Khac Vu
8. Yew-Soon Ong

In Industrial and Manufacturing Engineering

1. Claudia D'Ambrosio
2. Gerhard Venter
3. Vladimir Balabanov
4. Felipe Antonio Chegury Viana

In Management Science and Operations Research

1. Claudia D'Ambrosio
2. Gerhard Venter
3. Vladimir Balabanov
4. Youssef Hamadi
5. Leo Liberti
6. Ky Khac Vu
7. Felipe Antonio Chegury Viana
8. Yew-Soon Ong

Citations in the corpus

• Surrogate-based methods for black-box optimization (2017, methodology): “Moreover, when minimizing this measure over the reduced class of LHDs, we can obtain designs with very good properties and useful for many applications [9,27,53].”
• An algorithm for fast optimal Latin hypercube design of experiments (2010, methodology): “[24]; and (iii) the native MATLAB function lhsdesign [29].”