Generating Sequential Space-Filling Designs Using Genetic Algorithms and Monte Carlo Methods

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem.

[1]  Michel Daydé,et al.  High Performance Computing for Computational Science - VECPAR 2006, 7th International Conference, Rio de Janeiro, Brazil, June 10-13, 2006, Revised Selected and Invited Papers , 2007, VECPAR.

[2]  T. J. Mitchell,et al.  Exploratory designs for computational experiments , 1995 .

[3]  Dirk Gorissen,et al.  Adaptive Distributed Metamodeling , 2006, VECPAR.

[4]  V. R. Joseph,et al.  ORTHOGONAL-MAXIMIN LATIN HYPERCUBE DESIGNS , 2008 .

[5]  G. Rennen,et al.  Nested maximin Latin hypercube designs , 2009 .

[6]  Filip De Turck,et al.  Evolutionary Model Type Selection for Global Surrogate Modeling , 2009, J. Mach. Learn. Res..

[7]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[8]  M. E. Johnson,et al.  Minimax and maximin distance designs , 1990 .

[9]  Dick den Hertog,et al.  Maximin Latin Hypercube Designs in Two Dimensions , 2007, Oper. Res..

[10]  Timothy W. Simpson,et al.  Sampling Strategies for Computer Experiments: Design and Analysis , 2001 .

[11]  Dirk Gorissen,et al.  Sequential modeling of a low noise amplifier with neural networks and active learning , 2009, Neural Computing and Applications.

[12]  Kenny Q. Ye,et al.  Algorithmic construction of optimal symmetric Latin hypercube designs , 2000 .

[13]  Masashi Sugiyama,et al.  Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error , 2006, J. Mach. Learn. Res..

[14]  G. Venter,et al.  An algorithm for fast optimal Latin hypercube design of experiments , 2010 .

[15]  Robert Lehmensiek,et al.  Adaptive sampling applied to multivariate, multiple output rational interpolation models with application to microwave circuits , 2002 .