% FROM: Citations4SAT.bib via BibSplit on  Sat Sep 25 13:48:18 EDT 2004 
@article{2005-InfoSciences-Brglez,
   author = "F. Brglez and  X. Y. Li and M. Stallmann and B. Militzer",
   title  = "Evolutionary and Alternative Algorithms: Reliable Cost Predictions
           for Finding Optimal Solutions to the {LABS} Problem",
   journal = "{Information Sciences}",
year = "2005",
   note = "In print. For links to pre-prints, data sets and experimental testbed, see also
             http://\-www.cbl.ncsu.edu/\-publications/",
abstract = "
    The {\em low-autocorrelation binary sequence} (LABS) problem
    represents a major challenge to all search algorithms, with the
    evolutionary algorithms claiming the best results so far. However,
    the termination criteria for these types of stochastic algorithms
    are not well-defined and no reliable claims have been made about optimality.
    Our approach to find the optima of the LABS problem is based on
    combining three principles into a single method:
    (1) solver performance 
    experiments with problem sizes for which optimal solutions are known,
    (2) an asymptotic statistical analysis of  
    such experiments,
    (3) reliable predictions of the computational cost required to find optimal 
    solutions for larger problem sizes.
    The proposed methodology provides a well-defined termination criterion for
    evolutionary and alternative search algorithms alike.
",
URL    = "http://www.cbl.ncsu.edu/publications/2004-InfoSciences-Brglez/",
eprint = 
"http://www.cbl.ncsu.edu/publications/2005-InfoSciences-Brglez/2004-InfoSciences-Brglez.pdf",
URLcrossref = "{For context and related publications, see the labsBED home page:
                http://www.cbl.ncsu.edu/OpenExperiments/LABS/ }",
}