D. Ackley.
An empirical study of bit vector function optimizacion.
Genetic Algorithms and Simulated Annealing, pages 170-215, 1987.

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

H. C. Andersen and A. Ch. Tsoi.
A constructive algorithm for the training of a multilayer pereptron based on the genetic algorithm.
Complex Systems, 7(4):249-268, 1993.

H.-G. Beyer and K. Deb.
On self-adapting features in real-parameter evolutionary algorithms.
IEEE Transactions on evolutionary computation, 5(3):250-270, June 2001.

H. Bersini, M. Dorigo, S. Langerman, G. Seront, and L. M. Gambardella.
Results of the first international contest on evolutionary optimisation (1st iceo).
In Proceedings of IEEE International Conference on Evolutionary Computation, IEEE-EC 96, pages 611-615, Nagoya, Japan, May 20-22 1996. IEEE Press.

T. Bäck, D. Fogel, and Z. Michalewicz.
Handbook of Evolutionary Computation.
Institute of Physics Publishing Ltd, Bristol and Oxford University Press, New York, 1997.

G. Bebis, M. Georgiopoulos, and T. Kasparis.
Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization.
Neurocomputing, 17:167-194, 1997.

G. Bebis, S. Louis, Y. Varol, and A. Yfantis.
Genetic object recognition using combinations of views.
IEEE Transactions on Evolutionary Computation, 6(2):132, April 2002.

E. Bengoetxea and T. Miquélez.
Estimation of distribution algorithms: A new tool for evolutionary computation, volume 2 of Genetic algorithms and evolutionary computation, chapter Experimental result in function optimization with EDAs in continuous Domain.
Kluwer, d.e. goldberg edition, 2002.

L. Breiman.
Stacked regressions.
Machine Learning, 24(1):49-64, 1996.

T. Bäck and H. P. Schwefel.
An overview of evolutionary algorithms for parameter optimization.
Evolutionary Computation, 1(1):1-23, 1993.

J. H. Bäck.
Evolutionary Algorithms in Theory and Practice.
Oxford University Press, Oxford, 1996.

J.R. Cano, F. Herrera, and M. Lozano.
Using evolutionary algorithms as instance selection for data reduction in kdd: an experimental study.
IEEE Transactions on Evolutionary Computation, 7(6):561-575, December 2003.

K. Deb and R. B. Agrawal.
Simulated binary crossover for continuous search space.
Complex Systems, 9:115-148, 1995.

Y. Davidor.
Genetic Algorithms and Robotics: A Heuristic Strategy for Optimization, volume 1 of Robotics and Automated Systems.
World Scientific, 1991.

K. Deb and H. Beyer.
Self-adaptive genetic algorithms with simulated binary crossover.
Evolutionary Computation, 9(2):195-219, 2001.

O. J. Dunn and V. Clark.
Applied Statistics: Analysis of Variance and Regression.
Wiley, New York, 1974.

De 75
K. D. De Jong.
An analysis of the behavior of a class of genetic adaptive systems.
PhD thesis, Departament of Computer and Communication Sciences, University of Michigan, Ann Arbor, 1975.

L. C. W. Dixon.
Nonlinear optimization: A survey of the state of the art.
Software for Numerical Mathematics, pages 193-216, 1974.
Academic Press.

M. B. de Jong and W. Kosters.
Solving 3-sat using adaptive sampling.
In H.L. Poutré and J. van den Herik, editors, Proceedings of the Tenth Dutch/Belgian Artificial Intelligence Conference, pages 221-228, 1998.

A. Eiben and T. Bäck.
Multi-parent recombination operators in continuous search spaces.
Technical Report TR-97-01, Leiden University, 1997.

A. E. Eiben and Th. Bäck.
Empirical investigation of multi-parent recombination operators in evolution strategies.
Evolutionary Computation, 5(3):347-365, 1997.

L. J. Eshelman and J. D. Schaffer.
Real-coded genetic algorithms and interval-schemata.
In L. Darrell Whitley, editor, Foundation of Genetic Algorithms 2, pages 187C3.3.7:1-C3.3.7:8.-202, San Mateo, 1993. Morgan Kaufmann.

A.E. Eiben, J.K. van der Hauw, and J.I. van Hemert.
Graph coloring with adaptive evolutionary algorithms.
Journal of Heuristics, 4(1):25-46, June 1998.

D. B. Fogel.
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence.
IEEE Press, Piscataway, New Jork, 1995.

L. J. Fogel, A. J. Owens, and M. J. Walsh.
Artificial Intelligence Through Simulated Evolution.
John Wiley & Sons, 1966.

R. Fletcher and M. J. D. Powell.
A rapidly convergent descent method for minimization.
Computer Journal, (6):163-168, 1963.

J. H. Friedman.
An overview of predictive learning and function approximation.
In V. Cherkassky, J. H. Friedman, and H. Wechsler, editors, From Statistics to Neural Networks, Theory and Pattern Recognition Applications, volume 136 of NATO ASI Series F, pages 1-61. Springer-Verlag, 1994.

D. E. Goldberg and K. Deb.
A comparative analysis of selection schemes used in genetic algorithms.
In G. J. E. Rawlins, editor, Foundations of Genetic Algorithms, pages 69-93, San Mateo, CA, 1991. Morgan Kaufmann.

D. E. Goldberg.
Genetic Algorithms in Search, Optimization, and Machine Learning.
Addison-Wesley, New York, 1989.

D. E. Goldberg.
Sizing populations for serial and parallel genetic algorithms.
In J. Schaffer, editor, 3rd International Conference on Genetic Algorithms, pages 70-79, San Mateo, CA, 1989. Morgan Kaufmann.

D. E. Goldberg.
Real-coded genetic algorithms, virtual alphabets, and blocking.
Complex Systems, (5):139-167, 1991.

N. García-Pedrajas, C. Hervás-Martínez, and D. Ortiz-Boyer.
Cooperative coevolution of artificial neural network ensembles for pattern classification.
IEEE Transactions on Evolutionary Computation, 9(3):271-302, June 2005.

J. J. Grefenstette.
Optimization of control parameters for genetic algorithms.
IEEE Transactions on Systems, Mans, and Cybernetics, 16(1):122-128, 1986.

V. S. Gordon and D. Whitley.
Serial and parallel genetic algorithms as function optimizers.
In S. Forrest, editor, Fifth International Conference on Genetic Algorithms, pages 177-183. Morgan Kaufmann, 1993.

G. Hadley.
Nonlinear and Dynamics Programming.
Addison Wesley, 1964.

S. Hashem.
Optimal linear combinations of neural networks.
Neural Networks, 10(4):599-614, 1997.

F. Herrera, E. Herrera-Viedma, E. Lozano, and J. L. Verdegay.
Fuzzy tools to improve genetic algorithms.
In Second European Congress on Intelligent Techniques and Soft Computing, pages 1532-1539, 1994.

F. Herrera and M. Lozano.
Gradual distributed real-coded genetic algorithms.
IEEE Transactions on Evolutionary Computation, 4(1):43-63, April 2000.

F. Herrera, M. Lozano, and A. M. Sánchez.
A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study.
International Journal of Intelligent Systems, 18:309-338, 2003.

F. Herrera, M. Lozano, and J. L. Verdegay.
Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis.
Artificial Inteligence Review, (12):265-319, 1998.
Kluwer Academic Publisher. Printed in Netherlands.

C. Hervás-Martínez and D. Ortiz-Boyer.
Analizing the statistical features of cixl2 crossover offspring.
Soft Computing, 9(4):270-279, 2005.

J. H. Holland.
Adaptation in natural and artificial systems.
The University of Michigan Press, Ann Arbor, MI, 1975.

T. Johnson and P. Husbands.
System identification using genetic algorithms.
In Parallel Problem Solving from Nature, volume 496 of Lecture Notes in Computer Science, pages 85-89, Berlin, 1990. Springer-Verlag.

K. A De Jong and J. Sarma.
Generation gaps revisited.
In L. D. Whitley, editor, Foundations of Genetic Algorithms, volume 2, pages 19-28. Morgan Kaufmann, San Mateo, 1993.

H. Kita.
A comparison study of self-adaptation in evolution strategies and real-code genetic algorithms.
Evolutionary Computation, 9(2):223-241, 2001.

H. Kita, I. Ono, and S. Kobayashi.
Theoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms.
In IEEE International Conference on Evolutionary Computation ICEC'98, pages 529-534, Anchorage, Alaska, USA, 5-9 May 1998.

M. Kendall and S. Stuart.
The advanced theory of statistics, volume 1.
Charles GriOEn & Company, 1977.

L. Kuncheva.
Editing for the k-nearest neighbors rule by a genetic algorithm.
Pattern Recognition Letter, 16:809-814, 1995.

J. Kivinen and M. Warmuth.
Exponential gradient descent versus gradient descent for linear predictors.
Information and Computation, 132(1):1-63, January 1997.

P. Larrañaga, R. Etxeberria, J.A. Lozano, and J.M. Peña.
Optimization in continuous domains by learning and simulation of gaussian networks.
In A.S. Wu, editor, Proceeding of the 2000 Genetic and Evolutionary Computation Conference Workshop Program, pages 201-204, 2000.

H. Levene.
In Contributions to Probability and Statistics, chapter Essays in Honor of Harold Hotelling, pages 278-292.
Stanford University Press, 1960.

M. Leblanc and R. Tibshirani.
Combining estimates in regression and classification.
Technical report, Department of Statistics, University of Toronto, 1993.

Y. Liu, X. Yao, and T. Higuchi.
Evolutionary ensembles with negative correlation learning.
IEEE Transactions on Evolutionary Computation, 4(4):380-387, November 2000.

C. J. Merz.
A principal components approach to combining regression estimates.
Machine Learning, 36(1):9-32, July 1999.

C. J. Merz.
Using correspondence analysis to combine classifiers.
Machine Learning, 36(1):33-58, July 1999.

Z. Michalewicz.
Genetic Algorithms + Data Structures = Evolution Programs.
Springer-Verlag, New York, 1992.

R. G. Miller.
Simultaneous Statistical Inference.
Wiley, New York, 2 edition, 1981.

R. G. Miller.
Beyond ANOVA, Basics of Applied Statistics.
Chapman & Hall, London, 2 edition, 1996.

M. Mizumoto.
Pictorial representations of fuzzy connectives. part i: Cases of t-norms, t-conorms and averaging operators.
Fuzzy Sets Systems, 31:217-242, 1989.

H. Mühlenbein, T. Mahnig, and O. Rodriguez.
Schemata, distributions and graphical models in evolutionary optimazation.
Journal of Heuristics, (5):215-247, 1999.

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

D.E. Moriarty, A.C. Schultz, and J.J. Grefenstette.
Evolutionary algorithms for reinforcement learning.
Journal Artificial Intelligence Reserarch, 11, 1999.

G. F. Miller, P. M. Todd, and S. U. Hedge.
Designing neural networks.
Neural Networks, 4:53-60, 1991.

D. Ortiz-Boyer, C. Hervás-Martínez, and J. Muñoz-Pérez.
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.
Kluwer Academic Publishers, 2003.

I. Ono and S. Kobayashi.
A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover.
In 7th International Conference on Genetic Algorithms, pages 246-253, Michigan, USA, July 1997. Michigan State University, Morgan Kaufman.

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

I. Ono, S. Kobayashi, and K. Yoshida.
Optimal lens design by real-coded genetic algorithms using undx.
Computer methods in applied mechanics and engineering, (186):483-497, 2000.

S. S. Oren.
On the selection of parameters in self scaling variable metric algorithms.
Mathematical Programming, (7):351-367, 1974.

D. W. Opitz and J. W. Shavlik.
Actively searching for an effective neural network ensemble.
Connection Science, 8(3):337-353, 1996.

M. P. Perrone and L. N. Cooper.
When networks disagree: Ensemble methods for hybrid neural networks.
In R. J. Mammone, editor, Neural Networks for Speech and Image Processing, pages 126-142. Chapman - Hall, 1993.

L. A. Rastrigin.
Extremal control systems.
In Theoretical Foundations of Engineering Cybernetics Series. Moscow: Nauka, Russian, 1974.

I. Rechenberg.
Evolutionsstrategie-Optimierum technischer Systeme nach Prinzipien der biologischen Evolution.
PhD thesis, Stuttgart-Bad Cannstatt: Frommann-Holzboog, 1973.

H. H. Rosenbrock.
An automatic method for finding the greatest or least value of a function.
Computer Journal, (3):175-184, 1960.

G. Rudolph.
Convergence analysis of canonical genetic algorithms.
IEEE Transactions on Neural Networks, special issue on evolutionary computation, 5(1):96-101, 1994.

R. Salomon.
Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions.
BioSystems, (39):263-278, 1996.

K.H. Sedighi, K. Ashenayi, T.W. Manikas, R.L. Wainwright, and H.M. Tai.
Autonomous local path planning for a mobile robot using a genetic algorithm.
In IEEE Congress on Evolutionary Computation, 2004.

G. W. Snedecor and W. G. Cochran.
Statistical Methods.
Iowa State University Press, Ames, Iowa, 7 edition, 1980.

M. Singh, A. Chatterjee, and S. Chaudhury.
Matching structural shape descriptions using genetic algorithms.
Pattern Recognition, 30(9):1451-1462, 1997.

J. Schaffer, R. Caruana, L. Eshelman, and R. Das.
A study of control parameters affecting online performance of genetic algorithms for function optimization.
In J. Schaffer, editor, 3rd International Conference on Genetic Algorithms, pages 51-60, San Mateo, CA, 1989. Morgan Kaufmann.

H. P. Schwefel.
Numerical Optimization of Computer Models.
John Wiley & Sons, 1981.
English translation of Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie, 1977.

H. P. Schwefel.
Evolution and Optimum Seeking.
John Wiley & Sons, 1995.

A. J. C. Sharkey.
On combining artificial neural nets.
Connection Science, 8:299-313, 1996.

R. E. Smith.
Adaptively resizing populations: An algorithm and analysis.
In S. Forrest, editor, 5th International Conference on Genetic Algorithms, page 653, San Mateo, CA, 1993. Morgan Kaufmann.

E. Spedicato.
Computational experience with quasi-newton algorithms for minimization problems of moderately large size.
Technical Report CISE-N-175, Centro Informazioni Studi Esperienze, Segrate (Milano), Italy, 1975.

D. Schlierkamp-Voosen.
Strategy adaptation by competition.
In Second European Congress on Intelligent Techniques and Soft Computing, pages 1270-1274, 1994.

H. Satoh, M. Yamamura, and S. Kobayashi.
Minimal generation gap model for gas considering both exploration and exploitation.
In Proceeding of the IIZUKA: Methodologies for the Conception, Design, and Application of Intelligent Sstems, pages 494-497, 1996.

A. C. Tamhane and D. D. Dunlop.
Statistics and Data Analysis.
Prentice Hall, 2000.

O. Takahashi, H. Kita, and S. Kobayashi.
A distance dependent alternation model on real-coded genetic algorithms.
In IEEE International Conference on Systems, Man, and Cybernetics, pages 619-624, 1999.

H. M. Voigt, H. Mühlenbein, and D. Cvetkovic.
Fuzzy recombination for the breeder genetic algorithms.
In L Eshelman, editor, The 6th International Conference Genetic Algorithms, pages 104-111, San Mateo, CA, 1995. Morgan Kaufmann.

G. I. Webb.
Multiboosting: A technique for combining boosting and wagging.
Machine Learning, 40(2):159-196, August 2000.

D. H. Wolpert and W. G. Macready.
No free-lunch theorems for search.
Technical Report 95-02-010, Santa Fe Institute, 1995.

D. Whitley, K. Mathias, S. Rana, and J. Dzubera.
Building better test functions.
In L. Eshelman, editor, Sixth International Conference on Genetic Algorithms, pages 239-246. Morgan Kaufmann, 1995.

A. Wright.
Genetic algorithms for real parameter optimization.
In G. J. E. Rawlin, editor, Foundations of Genetic Algorithms 1, pages 205-218, San Mateo, 1991. Morgan Kaufmann.

B. T. Zhang and J. J. Kim.
Comparison of selection methods for evolutionary optimization.
Evolutionary Optimization, 2(1):55-70, 2000.

Z-H. Zhou, J. Wu, and W. Tang.
Ensembling neural networks: Many could be better than all.
Artificial Intelligence, 137(1-2):239-253, May 2002.

Domingo 2005-07-11