1 |
I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," Journal of Machine Leaning Research, vol. 3, pp. 1157-1182, 2003.
|
2 |
G. M. Furnival and R.W. Wilson, "Regression by leaps and bounds," Technometrics, vol. 16, pp. 416-423, 1974.
|
3 |
A. P. D. Silva, "Efficient variable screening for multivariate analysis," Journal of Multivariate Analysis, vol.76, pp. 35-62, 2001.
DOI
|
4 |
A. P. Duarte-Silva, "Discarding variables in a principal component analysis: algorithms for all-subsets comparisons," Computational Statistics, vol. 17 pp. 251-271, 2002.
DOI
|
5 |
C. Gatu and E. J. Kontoghiorghes, "Branch-and-bound algorithms for computing the best-subset regression models," Journal of Computational and Graphical Statistics, vol. 15, no. 1, pp. 139-156, 2006.
DOI
|
6 |
M. Hofmann, C. Gatu, and E. J. Kontoghiorghes, "Efficient algorithms for computing the best subset regression models for large-scale problems," Computational Statistics and Data Analysis, vol. 52, no. 1, pp. 16-29, 2007.
DOI
|
7 |
M. J. Brusco, D. Steinley, and J. D. Cradit, "An exact algorithm for hierarchically well-formulated subsets in second-order polynomial regression," Technometrics, vol. 51, no. 3, pp. 306-315, 2009.
DOI
|
8 |
J. Pacheco, S. Casado, and S. Porras, "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics and Data Analysis, vol. 57, no. 1, pp. 95-111, 2013.
DOI
|
9 |
Z. Drezner and G. A. Marcoulides, "Tabu seach model selection in multiple regression analysis," Communications in Statistics - Simulation and Computation, vol. 28, no. 9, pp. 349-367, 1999.
DOI
|
10 |
H. Hasan, "Subset selection in multiple linear regression models: A hybrid of genetic and simulated annealing algorithms," Applied Mathematics and Computation, vol. 219, no. 23, pp. 11018-11028, 2013.
DOI
|
11 |
N. R. Draper and H. Smith, Applied Regression Analysis, 3th Edition, NewYork: Wiley, 1998.
|
12 |
D. G. Montgomery and E. A Peck, Introduction to Linear Regression Analysis, 2nd Edition, NewYork: Wiley, 1992.
|
13 |
F. Glover, "Heuristics for integer programming using surrogate constraints", Decision Sciences, vol. 8, no. 1, pp. 156-166, 1977.
DOI
|
14 |
F. Glover, "Future paths for integer programming and links to artificial intelligence," Computers and Operations Research, vol. 13, no. 5, pp. 533-549, 1986.
DOI
|
15 |
S. Oliveira and G. Stroud, "A parallel version of tabu search and the assignment problem," Heuristics for Combinatorial Optimization, vol. 4, pp. 1-24, 1989.
|
16 |
D. D. Werra and A. Herz, "Tabu search techniques: a tutorial and an application to neural networks," OR Spektrum, vol. 11, pp. 131-141, 1989.
DOI
|
17 |
M. Laguna, J. W. Barnes, and F. Glover, "Tabu search methods for a single machine scheduling problem", Journal of Intelligent Manufacturing, vol. 2, no. 2, pp. 63-74, 1991.
DOI
|
18 |
M. Laguna and J. L. G. Velarde, "A search heuristic for just-in-time scheduling in parallel machines," Journal of Intelligent Manufacturing, vol. 2, no. 4, pp. 253-260, 1991.
DOI
|
19 |
F. T. Lin, C. Y. Kao, and C. C. Hsu, "Applying the genetic approach to simulated annealing in solving some NP-hard problems," IEEE Transactions on System Man Cybernetics, vol. 23, no. 6, pp. 1752-1767, 1993.
DOI
|
20 |
J. A. Bland and G. P. Dawson, "Tabu search and design optimization," Computer Aided Design, vol. 23, no. 3, pp. 195-202, 1991.
DOI
|
21 |
J. H. Holland, "Adaptaion in natural and artificial systems," University of Michigan Press, 1975.
|
22 |
S. Kirpatirck, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, pp. 671-680, 1983.
DOI
|
23 |
M. Widmer and A. Hertz, "A new heuristic method for the flow shop sequencing problem," European Journal of Operational Research, vol. 41, no. 2, pp. 186-193, 1989.
DOI
|
24 |
E. Tailard, "Some efficient heuristic methods for the flow shop sequencing problem," European Journal of Operational Research, vol. 47, no. 1, pp. 65-74, 1990.
DOI
|