Mixed Models for the Analysis of Optimization Algorithms

• Bang-Jensen J, Chiarandini M, Morling P (2009) A computational investigation of heuristic algorithms for 2-edge-connectivity augmentation.Networks (In print.)

  Google Scholar 

• Barr R, Golden B, Kelly J, Resende M, Stewart W (1995) Designing and reporting on computational experiments with heuristic methods.Journal of Heuristics 1(1):9–32

  Article  MATH  Google Scholar 

• Bates D (2007) Personal Communication

  Google Scholar 

• Bates D, Maechler M, Dai B (2008) lme4:Linear mixed-effects models using S4 classes. URL http://lme4.r-forge.r-project.org/, R package version 0.999375-28

• Birattari M (2004) On the estimation of the expected performance of a metaheuristic on a class of instances. How many instances, how many runs? Tech. Rep. TR/IRIDIA/2004-01, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

  Google Scholar 

• Bondy J, Murty U (2008) Graph Theory. Graduate Texts in Mathematics, Vol. 244,Springer

  Google Scholar 

• Chiarandini M (2005) Stochastic local search methods for highly constrained combinatorial optimisation problems. PhD thesis, Computer Science Department, Darmstadt University of Technology, Darmstadt, Germany

  Google Scholar 

• Coffin M, Saltzman MJ (2000)Statistical analysis of computational tests of algorithms and heuristics. INFORMS Journal on Computing 12(1):24–44

  Article  MATH  Google Scholar 

• Conforti M, Galluccio A, Proietti G (2004) Edge-connectivity augmentation and network matrices. In:Workshop on Graph-Theoretic Concepts in Computer Science, Springer, Lecture Notes in Computer Science, vol 3353, pp 355–364

  Chapter  Google Scholar 

• Cormen T, Leiserson C, Rivest R (2001) Introduction to Algorithms, 2nd edn.MIT press

  MATH  Google Scholar 

• Czarn A, MacNish C, Vijayan K, Turlach B, Gupta R (2004) Statistical exploratory analysis of genetic algorithms. Evolutionary Computation, IEEE Transactions on 8(4):405–421

  Article  Google Scholar 

• Fox J (2002) Linear mixed models. Appendix to An R and S-PLUS Companion to Applied Regression,URL http://cran.r-project.org/doc/ contrib/Fox-Companion/appendix-mixed-models.pdf

• Glover F, Kochenberger G (eds) (2002) Handbook of Metaheuristics, International Series in Operations Research & Management Science, vol 57. Kluwer Academic, Norwell, MA

  Google Scholar 

• Hooker JN (1996) Testing heuristics: We have it all wrong. Journal of Heuristics 1:32–42

  Google Scholar 

• Johnson R, Wichern D (2007) Applied Multivariate Statistical Analysis, 6th edn.Prentice-Hall International

  MATH  Google Scholar 

• MKutner MH, Nachtsheim CJ, Neter J, Li W (2005) Applied Linear Statistical Models, 5th edn. McGraw-Hill

  Google Scholar 

• Lehmann E (2003) Theory of point estimation.Springer

  Google Scholar 

• Lehmann E, Romano J (2008) Testing statistical hypothesis.Springer

  Google Scholar 

• Lenth RV (2006) Java applets for power and sample size [computer software]. Retrieved 29 January 2009 from http://www.stat.uiowa.edu/˜rlenth/ Power

• Lin BW, Rardin RL (1979) Controlled experimental design for statistical comparison of integer programming algorithms. Management Science 25(12):1258–1271

  Article  MathSciNet  Google Scholar 

• McGeoch CC (1996) Toward an experimental method for algorithm simulation. INFORMS Journal on Computing 8(1):1–15, this journal issue contains also commentaries by Pierre L’Ecuyer, James B. Orlin and Douglas R. Shier, and a rejoinder by C. McGeoch

  Article  MATH  MathSciNet  Google Scholar 

• Michiels W, Aarts E, Korst J (2007) Theoretical Aspects of Local Search. Monographs in Theoretical Computer Science, An EATCS Series, Springer

  Google Scholar 

• Molenberghs G, Verbeke G (eds) (1997)Linear Mixed Models in Practice - A SASOriented Approach. >Springer

  Google Scholar 

• Molenberghs G, Verbeke G (2005) Models for Discrete Longitudinal Data. Springer

  MATH  Google Scholar 

• Montgomery DC (2005) Design and Analysis of Experiments, 6th edn. Wiley

  MATH  Google Scholar 

• Pinheiro J, Bates D, DebRoy S, Sarkar D, the R Core team (2008) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–89

  Google Scholar 

• Pinheiro JC, Bates DM (2000) Mixed-Effects Models in S and S-Plus. Springer

  Book  MATH  Google Scholar 

• R Development Core Team (2008) R: A Language and Environment for StatisticalComputing. R Foundation for Statistical Computing, Vienna, Austria, URL http://www.R-project.org

• Rardin RL, Uzsoy R (2001) Experimental evaluation of heuristic optimization algorithms: A tutorial. Journal of Heuristics 7(3):261–304

  Article  MATH  Google Scholar 

• Rousseeuw PJ (1984) Least median of squares regression. Journal of the American Statistical Association 79(388):871–880, URL http://www.jstor.org/ stable/2288718 SAS Institute Inc. (2007) SAS online documentation: Parameterization of mixed models. http://www.webcitation.org/5h0u00trT, retrieved on 2009-05-24

• Stram D, Lee J (1994) Variance components testing in the longitudinal mixed effects model. Biometrics 50(4):1171–1177

  Article  MATH  Google Scholar 

• Stram D, Lee J (1995) Correction to ’Variance components testing in the longitudinal mixed effects model’. Biometrics 51(3):1196

  Google Scholar 

• Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1):67–82

  Article  Google Scholar 

• Zanakis SH (1977) Heuristic 0-1 linear programming: An experimental comparison of three methods. Management Science 24(1):91–104

  Article  MATH  MathSciNet  Google Scholar 

• Zemel E (1981) Measuring the quality of approximate solutions to zero-one programming problems. Mathematics of Operations Research 6(3):319–332

  Article  MATH  MathSciNet  Google Scholar 

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