\newcommand{\etalchar}[1]{$^{#1}$}
\begin{thebibliography}{OBHMMP03}

\bibitem[Ack87]{Ackley87}
D.~Ackley.
\newblock An empirical study of bit vector function optimizacion.
\newblock {\em Genetic Algorithms and Simulated Annealing}, pages 170--215,
  1987.

\bibitem[AMM94]{Arabas94}
J.~Arabas, Z.~Michalewicz, and J.~Mulawka.
\newblock Gavaps - a genetic algorithm with varying population size.
\newblock In Z.~Michalewicz, J.~Krawczyk, M.~Kazemi, and C.~Janikow, editors,
  {\em First IEEE International Conference on Evolutionary Computation},
  volume~1, pages 73--78, Orlando, 27-29 June 1994. IEEE Service Center,
  Piscataway, NJ.

\bibitem[AT93]{andersen93}
H.~C. Andersen and A.~Ch. Tsoi.
\newblock A constructive algorithm for the training of a multilayer pereptron
  based on the genetic algorithm.
\newblock {\em Complex Systems}, 7(4):249--268, 1993.

\bibitem[BD01]{Beyer2001}
H.-G. Beyer and K.~Deb.
\newblock On self-adapting features in real-parameter evolutionary algorithms.
\newblock {\em IEEE Transactions on evolutionary computation}, 5(3):250--270,
  June 2001.

\bibitem[BDL{\etalchar{+}}96]{Bersini96}
H.~Bersini, M.~Dorigo, S.~Langerman, G.~Seront, and L.~M. Gambardella.
\newblock Results of the first international contest on evolutionary
  optimisation (1st iceo).
\newblock In {\em Proceedings of IEEE International Conference on Evolutionary
  Computation, IEEE-EC 96}, pages 611--615, Nagoya, Japan, May 20-22 1996. IEEE
  Press.

\bibitem[BFM97]{Back97}
T.~Bäck, D.~Fogel, and Z.~Michalewicz.
\newblock {\em Handbook of Evolutionary Computation}.
\newblock Institute of Physics Publishing Ltd, Bristol and Oxford University
  Press, New York, 1997.

\bibitem[BGK97]{bebis97}
G.~Bebis, M.~Georgiopoulos, and T.~Kasparis.
\newblock Coupling weight elimination with genetic algorithms to reduce network
  size and preserve generalization.
\newblock {\em Neurocomputing}, 17:167--194, 1997.

\bibitem[BLVY02]{Bebis2002}
G.~Bebis, S.~Louis, Y.~Varol, and A.~Yfantis.
\newblock Genetic object recognition using combinations of views.
\newblock {\em IEEE Transactions on Evolutionary Computation}, 6(2):132, April
  2002.

\bibitem[BM02]{Bengoetxea2002}
E.~Bengoetxea and T.~Miquélez.
\newblock {\em Estimation of distribution algorithms: A new tool for
  evolutionary computation}, volume~2 of {\em Genetic algorithms and
  evolutionary computation}, chapter Experimental result in function
  optimization with EDAs in continuous Domain.
\newblock Kluwer, d.e. goldberg edition, 2002.

\bibitem[Bre96]{breiman96}
L.~Breiman.
\newblock Stacked regressions.
\newblock {\em Machine Learning}, 24(1):49--64, 1996.

\bibitem[BS93]{Back93}
T.~Bäck and H.~P. Schwefel.
\newblock An overview of evolutionary algorithms for parameter optimization.
\newblock {\em Evolutionary Computation}, 1(1):1--23, 1993.

\bibitem[Bäc96]{Back96}
J.~H. Bäck.
\newblock {\em Evolutionary Algorithms in Theory and Practice}.
\newblock Oxford University Press, Oxford, 1996.

\bibitem[CHL03]{Cano2003}
J.R. Cano, F.~Herrera, and M.~Lozano.
\newblock Using evolutionary algorithms as instance selection for data
  reduction in kdd: an experimental study.
\newblock {\em IEEE Transactions on Evolutionary Computation}, 7(6):561--575,
  December 2003.

\bibitem[DA95]{Deb95}
K.~Deb and R.~B. Agrawal.
\newblock Simulated binary crossover for continuous search space.
\newblock {\em Complex Systems}, 9:115--148, 1995.

\bibitem[Dav91]{Davidor94}
Y.~Davidor.
\newblock {\em Genetic Algorithms and Robotics: A Heuristic Strategy for
  Optimization}, volume~1 of {\em Robotics and Automated Systems}.
\newblock World Scientific, 1991.

\bibitem[DB01]{Deb2001}
K.~Deb and H.~Beyer.
\newblock Self-adaptive genetic algorithms with simulated binary crossover.
\newblock {\em Evolutionary Computation}, 9(2):195--219, 2001.

\bibitem[DC74]{Dunn74}
O.~J. Dunn and V.~Clark.
\newblock {\em Applied Statistics: Analysis of Variance and Regression}.
\newblock Wiley, New York, 1974.

\bibitem[{De }75]{DeJong75}
K.~D. {De Jong}.
\newblock {\em An analysis of the behavior of a class of genetic adaptive
  systems}.
\newblock PhD thesis, Departament of Computer and Communication Sciences,
  University of Michigan, Ann Arbor, 1975.

\bibitem[Dix74]{Dixon74}
L.~C.~W. Dixon.
\newblock Nonlinear optimization: A survey of the state of the art.
\newblock {\em Software for Numerical Mathematics}, pages 193--216, 1974.
\newblock Academic Press.

\bibitem[dK98]{DeJongM98}
M.~B. {de Jong} and W.~Kosters.
\newblock Solving 3-sat using adaptive sampling.
\newblock In H.L. Poutré and J.~{van den Herik}, editors, {\em Proceedings of
  the Tenth Dutch/Belgian Artificial Intelligence Conference}, pages 221--228,
  1998.

\bibitem[EB97a]{Eiben97}
A.~Eiben and T.~Bäck.
\newblock Multi-parent recombination operators in continuous search spaces.
\newblock Technical Report TR-97-01, Leiden University, 1997.

\bibitem[EB97b]{Eiben97c}
A.~E. Eiben and Th. Bäck.
\newblock Empirical investigation of multi-parent recombination operators in
  evolution strategies.
\newblock {\em Evolutionary Computation}, 5(3):347--365, 1997.

\bibitem[ES93]{Eshelman93}
L.~J. Eshelman and J.~D. Schaffer.
\newblock Real-coded genetic algorithms and interval-schemata.
\newblock In L.~Darrell Whitley, editor, {\em Foundation of Genetic Algorithms
  2}, pages 187C3.3.7:1--C3.3.7:8.--202, San Mateo, 1993. Morgan Kaufmann.

\bibitem[Evv98]{Eiben98}
A.E. Eiben, J.K. {van der Hauw}, and J.I. {van Hemert}.
\newblock Graph coloring with adaptive evolutionary algorithms.
\newblock {\em Journal of Heuristics}, 4(1):25--46, June 1998.

\bibitem[Fog95]{Fogel95}
D.~B. Fogel.
\newblock {\em Evolutionary Computation: Toward a New Philosophy of Machine
  Intelligence}.
\newblock IEEE Press, Piscataway, New Jork, 1995.

\bibitem[FOW66]{Fogel66}
L.~J. Fogel, A.~J. Owens, and M.~J. Walsh.
\newblock {\em Artificial Intelligence Through Simulated Evolution}.
\newblock John Wiley \& Sons, 1966.

\bibitem[FP63]{Fletcher63}
R.~Fletcher and M.~J.~D. Powell.
\newblock A rapidly convergent descent method for minimization.
\newblock {\em Computer Journal}, (6):163--168, 1963.

\bibitem[Fri94]{Friedman94}
J.~H. Friedman.
\newblock An overview of predictive learning and function approximation.
\newblock In V.~Cherkassky, J.~H. Friedman, and H.~Wechsler, editors, {\em From
  Statistics to Neural Networks, Theory and Pattern Recognition Applications},
  volume 136 of {\em NATO ASI Series F}, pages 1--61. Springer-Verlag, 1994.

\bibitem[GD91]{Goldberg91b}
D.~E. Goldberg and K.~Deb.
\newblock A comparative analysis of selection schemes used in genetic
  algorithms.
\newblock In G.~J.~E. Rawlins, editor, {\em Foundations of Genetic Algorithms},
  pages 69--93, San Mateo, CA, 1991. Morgan Kaufmann.

\bibitem[Gol89a]{Goldberg89a}
D.~E. Goldberg.
\newblock {\em Genetic Algorithms in Search, Optimization, and Machine
  Learning}.
\newblock Addison-Wesley, New York, 1989.

\bibitem[Gol89b]{Goldberg89c}
D.~E. Goldberg.
\newblock Sizing populations for serial and parallel genetic algorithms.
\newblock In J.~Schaffer, editor, {\em 3rd International Conference on Genetic
  Algorithms}, pages 70--79, San Mateo, CA, 1989. Morgan Kaufmann.

\bibitem[Gol91]{Goldberg91}
D.~E. Goldberg.
\newblock Real-coded genetic algorithms, virtual alphabets, and blocking.
\newblock {\em Complex Systems}, (5):139--167, 1991.

\bibitem[GPHMOB05]{Garcia2005}
N.~García-Pedrajas, C.~Hervás-Martínez, and D.~Ortiz-Boyer.
\newblock Cooperative coevolution of artificial neural network ensembles for
  pattern classification.
\newblock {\em IEEE Transactions on Evolutionary Computation}, 9(3):271--302,
  June 2005.

\bibitem[Gre86]{Grefenstette86}
J.~J. Grefenstette.
\newblock Optimization of control parameters for genetic algorithms.
\newblock {\em IEEE Transactions on Systems, Mans, and Cybernetics},
  16(1):122--128, 1986.

\bibitem[GW93]{Gordon93}
V.~S. Gordon and D.~Whitley.
\newblock Serial and parallel genetic algorithms as function optimizers.
\newblock In S.~Forrest, editor, {\em Fifth International Conference on Genetic
  Algorithms}, pages 177--183. Morgan Kaufmann, 1993.

\bibitem[Had64]{Hadley64}
G.~Hadley.
\newblock {\em Nonlinear and Dynamics Programming}.
\newblock Addison Wesley, 1964.

\bibitem[Has97]{hashem97}
S.~Hashem.
\newblock Optimal linear combinations of neural networks.
\newblock {\em Neural Networks}, 10(4):599--614, 1997.

\bibitem[HHVLV94]{Herrera94}
F.~Herrera, E.~Herrera-Viedma, E.~Lozano, and J.~L. Verdegay.
\newblock Fuzzy tools to improve genetic algorithms.
\newblock In {\em Second European Congress on Intelligent Techniques and Soft
  Computing}, pages 1532--1539, 1994.

\bibitem[HL00]{Herrera2000}
F.~Herrera and M.~Lozano.
\newblock Gradual distributed real-coded genetic algorithms.
\newblock {\em IEEE Transactions on Evolutionary Computation}, 4(1):43--63,
  April 2000.

\bibitem[HLS03]{Herrera2003}
F.~Herrera, M.~Lozano, and A.~M. Sánchez.
\newblock A taxonomy for the crossover operator for real-coded genetic
  algorithms: An experimental study.
\newblock {\em International Journal of Intelligent Systems}, 18:309--338,
  2003.

\bibitem[HLV98]{Herrera98}
F.~Herrera, M.~Lozano, and J.~L. Verdegay.
\newblock Tackling real-coded genetic algorithms: Operators and tools for
  behavioural analysis.
\newblock {\em Artificial Inteligence Review}, (12):265--319, 1998.
\newblock Kluwer Academic Publisher. Printed in Netherlands.

\bibitem[HMOB05]{Hervas2005}
C.~Hervás-Martínez and D.~Ortiz-Boyer.
\newblock Analizing the statistical features of cixl2 crossover offspring.
\newblock {\em Soft Computing}, 9(4):270--279, 2005.

\bibitem[Hol75]{Holland75}
J.~H. Holland.
\newblock {\em Adaptation in natural and artificial systems}.
\newblock The University of Michigan Press, Ann Arbor, MI, 1975.

\bibitem[JH90]{Johnson90}
T.~Johnson and P.~Husbands.
\newblock System identification using genetic algorithms.
\newblock In {\em Parallel Problem Solving from Nature}, volume 496 of {\em
  Lecture Notes in Computer Science}, pages 85--89, Berlin, 1990.
  Springer-Verlag.

\bibitem[JS93]{DeJong93}
K.~A~De Jong and J.~Sarma.
\newblock Generation gaps revisited.
\newblock In L.~D. Whitley, editor, {\em Foundations of Genetic Algorithms},
  volume~2, pages 19--28. Morgan Kaufmann, San Mateo, 1993.

\bibitem[Kit01]{Kita2001}
H.~Kita.
\newblock A comparison study of self-adaptation in evolution strategies and
  real-code genetic algorithms.
\newblock {\em Evolutionary Computation}, 9(2):223--241, 2001.

\bibitem[KOK98]{Kita98}
H.~Kita, I.~Ono, and S.~Kobayashi.
\newblock Theoretical analysis of the unimodal normal distribution crossover
  for real-coded genetic algorithms.
\newblock In {\em IEEE International Conference on Evolutionary Computation
  ICEC'98}, pages 529--534, Anchorage, Alaska, USA, 5-9 May 1998.

\bibitem[KS77]{Kendall77}
M.~Kendall and S.~Stuart.
\newblock {\em The advanced theory of statistics}, volume~1.
\newblock Charles GriOEn \& Company, 1977.

\bibitem[Kun95]{Kuncheva95}
L.~Kuncheva.
\newblock Editing for the k-nearest neighbors rule by a genetic algorithm.
\newblock {\em Pattern Recognition Letter}, 16:809--814, 1995.

\bibitem[KW97]{kivinen97}
J.~Kivinen and M.~Warmuth.
\newblock Exponential gradient descent versus gradient descent for linear
  predictors.
\newblock {\em Information and Computation}, 132(1):1--63, January 1997.

\bibitem[LELP00]{Larranaga2000b}
P.~Larrañaga, R.~Etxeberria, J.A. Lozano, and J.M. Peña.
\newblock Optimization in continuous domains by learning and simulation of
  gaussian networks.
\newblock In A.S. Wu, editor, {\em Proceeding of the 2000 Genetic and
  Evolutionary Computation Conference Workshop Program}, pages 201--204, 2000.

\bibitem[Lev60]{Levene60}
H.~Levene.
\newblock {\em In Contributions to Probability and Statistics}, chapter Essays
  in Honor of Harold Hotelling, pages 278--292.
\newblock Stanford University Press, 1960.

\bibitem[LT93]{leblanc93}
M.~Leblanc and R.~Tibshirani.
\newblock Combining estimates in regression and classification.
\newblock Technical report, Department of Statistics, University of Toronto,
  1993.

\bibitem[LYH00]{liu00}
Y.~Liu, X.~Yao, and T.~Higuchi.
\newblock Evolutionary ensembles with negative correlation learning.
\newblock {\em IEEE Transactions on Evolutionary Computation}, 4(4):380--387,
  November 2000.

\bibitem[Mer99a]{merz99}
C.~J. Merz.
\newblock A principal components approach to combining regression estimates.
\newblock {\em Machine Learning}, 36(1):9--32, July 1999.

\bibitem[Mer99b]{merz99a}
C.~J. Merz.
\newblock Using correspondence analysis to combine classifiers.
\newblock {\em Machine Learning}, 36(1):33--58, July 1999.

\bibitem[Mic92]{Michalewicz92}
Z.~Michalewicz.
\newblock {\em Genetic Algorithms + Data Structures = Evolution Programs}.
\newblock Springer-Verlag, New York, 1992.

\bibitem[Mil81]{Miller81}
R.~G. Miller.
\newblock {\em Simultaneous Statistical Inference}.
\newblock Wiley, New York, 2 edition, 1981.

\bibitem[Mil96]{Miller96}
R.~G. Miller.
\newblock {\em Beyond ANOVA, Basics of Applied Statistics}.
\newblock Chapman {\&} Hall, London, 2 edition, 1996.

\bibitem[Miz89]{Mizumoto89}
M.~Mizumoto.
\newblock Pictorial representations of fuzzy connectives. part i: Cases of {\it
  t}-norms, {\it t}-conorms and averaging operators.
\newblock {\em Fuzzy Sets Systems}, 31:217--242, 1989.

\bibitem[MMR99]{Muhlenbein99}
H.~Mühlenbein, T.~Mahnig, and O.~Rodriguez.
\newblock Schemata, distributions and graphical models in evolutionary
  optimazation.
\newblock {\em Journal of Heuristics}, (5):215--247, 1999.

\bibitem[MP98]{Muhlenbein98}
H.~Mühlenbein and G.~Paa$\beta$.
\newblock From recombination of genes to the estimation of distributions i.
  binary parameters.
\newblock In A.~E. Eiben, T.~Bäck, M.~Schoenauer, and H.-P. Schwefel, editors,
  {\em The 5th Conference on Parallel Problem Solving from Nature}, pages
  178--187. Springer, 1998.

\bibitem[MSG99]{Moriarty99}
D.E. Moriarty, A.C. Schultz, and J.J. Grefenstette.
\newblock Evolutionary algorithms for reinforcement learning.
\newblock {\em Journal Artificial Intelligence Reserarch}, 11, 1999.

\bibitem[MTH91]{miller91}
G.~F. Miller, P.~M. Todd, and S.~U. Hedge.
\newblock Designing neural networks.
\newblock {\em Neural Networks}, 4:53--60, 1991.

\bibitem[OBHMMP03]{Ortiz2003}
D.~Ortiz-Boyer, C.~Hervás-Martínez, and J.~Muñoz-Pérez.
\newblock {\em Metaheuristics: Computer Decision-Making}, chapter Study of
  genetic algorithms with crossover based on confidence intervals as an
  alternative to classic least squares estimation methods for non-linear
  models, pages 127--151.
\newblock Kluwer Academic Publishers, 2003.

\bibitem[OK97]{Ono97}
I.~Ono and S.~Kobayashi.
\newblock A real-coded genetic algorithm for function optimization using
  unimodal normal distribution crossover.
\newblock In {\em 7th International Conference on Genetic Algorithms}, pages
  246--253, Michigan, USA, July 1997. Michigan State University, Morgan
  Kaufman.

\bibitem[OKK99]{Ono99}
I.~Ono, H.~Kita, and S.~Kobayashi.
\newblock A robust real-coded genetic algorithm using unimodal normal
  distribution crossover augmented by uniform crossover: Effects of
  self-adaptation of crossover probabilities.
\newblock In Wolfgang Banzhaf, Jason Daida, Agoston~E. Eiben, Max~H. Garzon,
  Vasant Honavar, Mark Jakiela, and Robert~E. Smith, editors, {\em Genetic and
  Evolutionary Computation Conf. (GECCO'99)}, pages 496--503, San Francisco,
  CA, 1999. Morgan Kaufmann.

\bibitem[OKY00]{Ono2000}
I.~Ono, S.~Kobayashi, and K.~Yoshida.
\newblock Optimal lens design by real-coded genetic algorithms using undx.
\newblock {\em Computer methods in applied mechanics and engineering},
  (186):483--497, 2000.

\bibitem[Ore74]{Oren74}
S.~S. Oren.
\newblock On the selection of parameters in self scaling variable metric
  algorithms.
\newblock {\em Mathematical Programming}, (7):351--367, 1974.

\bibitem[OS96]{opitz96}
D.~W. Opitz and J.~W. Shavlik.
\newblock Actively searching for an effective neural network ensemble.
\newblock {\em Connection Science}, 8(3):337--353, 1996.

\bibitem[PC93]{perrone93}
M.~P. Perrone and L.~N. Cooper.
\newblock When networks disagree: Ensemble methods for hybrid neural networks.
\newblock In R.~J. Mammone, editor, {\em Neural Networks for Speech and Image
  Processing}, pages 126--142. Chapman -- Hall, 1993.

\bibitem[Ras74]{Rastrigin74}
L.~A. Rastrigin.
\newblock Extremal control systems.
\newblock In {\em Theoretical Foundations of Engineering Cybernetics Series}.
  Moscow: Nauka, Russian, 1974.

\bibitem[Rec73]{Rechenberg73}
I.~Rechenberg.
\newblock {\em Evolutionsstrategie-Optimierum technischer Systeme nach
  Prinzipien der biologischen Evolution}.
\newblock PhD thesis, Stuttgart-Bad Cannstatt: Frommann-Holzboog, 1973.

\bibitem[Ros60]{Rosenbrock60}
H.~H. Rosenbrock.
\newblock An automatic method for finding the greatest or least value of a
  function.
\newblock {\em Computer Journal}, (3):175--184, 1960.

\bibitem[Rud94]{Rudolph94}
G.~Rudolph.
\newblock Convergence analysis of canonical genetic algorithms.
\newblock {\em IEEE Transactions on Neural Networks, special issue on
  evolutionary computation}, 5(1):96--101, 1994.

\bibitem[Sal96]{Salomon96}
R.~Salomon.
\newblock Reevaluating genetic algorithm performance under coordinate rotation
  of benchmark functions.
\newblock {\em BioSystems}, (39):263--278, 1996.

\bibitem[SAM{\etalchar{+}}04]{Sedighi2004}
K.H. Sedighi, K.~Ashenayi, T.W. Manikas, R.L. Wainwright, and H.M. Tai.
\newblock Autonomous local path planning for a mobile robot using a genetic
  algorithm.
\newblock In {\em IEEE Congress on Evolutionary Computation}, 2004.

\bibitem[SC80]{Snedecor80}
G.~W. Snedecor and W.~G. Cochran.
\newblock {\em Statistical Methods}.
\newblock Iowa State University Press, Ames, Iowa, 7 edition, 1980.

\bibitem[SCC97]{Singh97}
M.~Singh, A.~Chatterjee, and S.~Chaudhury.
\newblock Matching structural shape descriptions using genetic algorithms.
\newblock {\em Pattern Recognition}, 30(9):1451--1462, 1997.

\bibitem[SCED89]{Schaffer89}
J.~Schaffer, R.~Caruana, L.~Eshelman, and R.~Das.
\newblock A study of control parameters affecting online performance of genetic
  algorithms for function optimization.
\newblock In J.~Schaffer, editor, {\em 3rd International Conference on Genetic
  Algorithms}, pages 51--60, San Mateo, CA, 1989. Morgan Kaufmann.

\bibitem[Sch81]{Schwefel81}
H.~P. Schwefel.
\newblock {\em Numerical Optimization of Computer Models}.
\newblock John Wiley \& Sons, 1981.
\newblock English translation of Numerische Optimierung von Computer-Modellen
  mittels der Evolutionsstrategie, 1977.

\bibitem[Sch95]{Schwefel95}
H.~P. Schwefel.
\newblock {\em Evolution and Optimum Seeking}.
\newblock John Wiley \& Sons, 1995.

\bibitem[Sha96]{sharkey96}
A.~J.~C. Sharkey.
\newblock On combining artificial neural nets.
\newblock {\em Connection Science}, 8:299--313, 1996.

\bibitem[Smi93]{Smith93}
R.~E. Smith.
\newblock Adaptively resizing populations: An algorithm and analysis.
\newblock In S.~Forrest, editor, {\em 5th International Conference on Genetic
  Algorithms}, page 653, San Mateo, CA, 1993. Morgan Kaufmann.

\bibitem[Spe75]{Spedicato75}
E.~Spedicato.
\newblock Computational experience with quasi-newton algorithms for
  minimization problems of moderately large size.
\newblock Technical Report CISE-N-175, Centro Informazioni Studi Esperienze,
  Segrate (Milano), Italy, 1975.

\bibitem[SV94]{SchlierkampVoosen94}
D.~Schlierkamp-Voosen.
\newblock Strategy adaptation by competition.
\newblock In {\em Second European Congress on Intelligent Techniques and Soft
  Computing}, pages 1270--1274, 1994.

\bibitem[SYK96]{Satoh96}
H.~Satoh, M.~Yamamura, and S.~Kobayashi.
\newblock Minimal generation gap model for gas considering both exploration and
  exploitation.
\newblock In {\em Proceeding of the IIZUKA: Methodologies for the Conception,
  Design, and Application of Intelligent Sstems}, pages 494--497, 1996.

\bibitem[TD00]{Tamhane2000}
A.~C. Tamhane and D.~D. Dunlop.
\newblock {\em Statistics and Data Analysis}.
\newblock Prentice Hall, 2000.

\bibitem[TKK99]{Takahashi99}
O.~Takahashi, H.~Kita, and S.~Kobayashi.
\newblock A distance dependent alternation model on real-coded genetic
  algorithms.
\newblock In {\em IEEE International Conference on Systems, Man, and
  Cybernetics}, pages 619--624, 1999.

\bibitem[VMC95]{Voigt95}
H.~M. Voigt, H.~Mühlenbein, and D.~Cvetkovic.
\newblock Fuzzy recombination for the breeder genetic algorithms.
\newblock In L~Eshelman, editor, {\em The 6th International Conference Genetic
  Algorithms}, pages 104--111, San Mateo, CA, 1995. Morgan Kaufmann.

\bibitem[Web00]{webb00}
G.~I. Webb.
\newblock Multiboosting: A technique for combining boosting and wagging.
\newblock {\em Machine Learning}, 40(2):159--196, August 2000.

\bibitem[WM95]{Wolpert95}
D.~H. Wolpert and W.~G. Macready.
\newblock No free-lunch theorems for search.
\newblock Technical Report 95-02-010, Santa Fe Institute, 1995.

\bibitem[WMRD95]{Whitley95}
D.~Whitley, K.~Mathias, S.~Rana, and J.~Dzubera.
\newblock Building better test functions.
\newblock In L.~Eshelman, editor, {\em Sixth International Conference on
  Genetic Algorithms}, pages 239--246. Morgan Kaufmann, 1995.

\bibitem[Wri91]{Wright91}
A.~Wright.
\newblock Genetic algorithms for real parameter optimization.
\newblock In G.~J.~E. Rawlin, editor, {\em Foundations of Genetic Algorithms
  1}, pages 205--218, San Mateo, 1991. Morgan Kaufmann.

\bibitem[ZK00]{Zhang2000}
B.~T. Zhang and J.~J. Kim.
\newblock Comparison of selection methods for evolutionary optimization.
\newblock {\em Evolutionary Optimization}, 2(1):55--70, 2000.

\bibitem[ZWT02]{zhou02}
Z-H. Zhou, J.~Wu, and W.~Tang.
\newblock Ensembling neural networks: Many could be better than all.
\newblock {\em Artificial Intelligence}, 137(1--2):239--253, May 2002.

\end{thebibliography}
