. Dans-le-cas-homogène, la série des probabilités de transition est une constante, elle est donc divergente. Alors, d'après le lemme de Borel-Cantelli, quand la somme des probabilités d'apparition des termes d'une suite est infinie, tous lesétatslesétats sont visités avec une probabilité de 1. En particulier, on passera dans le sous-ensemble desétatsdesétats absorbants. On peut donc

. Cependant, en ajoutant une hypothèse peu contraignante sur la fonction p m (t), nous allons pouvoir appliquer le même raisonnement

S. Ensuite and . On, construit une série constante : u(t) = f (µ) pour tout t ? N alors, ?t ? N, p ij

E. H. Aarts and P. J. Van-laarhoven, Statistical cooling : A general Approach to combinatorial optimization problems, Phillips Journal of Research, vol.40, pp.193-226, 1985.

E. H. Aarts and J. Korst, Simulated Annealing and Boltzmann Machines : A Stochastic Approach to Combinatorial Optimization and Neural Computing, 1989.

G. Alexe, S. Alexe, L. A. Liotta, E. Petricoin, M. Reiss et al., Ovarian cancer detection by logical analysis of proteomic data, PROTEOMICS, vol.4, issue.3, pp.766-783, 2004.
DOI : 10.1002/pmic.200300574

J. Antonisse, A New Interpretation of Schema Notation that Overturns the Binary Encoding Constraint, Proceedings of the Third International Conference on GAs, 1989.

K. A. Baggerly, J. S. Morris, J. Wang, D. Gold, L. Xiao et al., A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples, PROTEOMICS, vol.3, issue.9, pp.1667-1672, 2003.
DOI : 10.1002/pmic.200300522

J. E. Baker, Reducing bias and inefficiency in the selection algorithm, Proceedings of the 2nd International Conference on Genetic Algorithms, pp.14-21, 1987.

M. Barker and W. Rayens, Partial least squares for discrimination, Journal of Chemometrics, vol.10, issue.3, pp.166-173, 2003.
DOI : 10.1002/cem.785

C. Bauchart, D. Remond, C. Chambon, P. Patureau-mirand, I. Savary-auzeloux et al., Small peptides (<5kDa) found in ready-to-eat beef meat, Meat Science, vol.74, issue.4, pp.658-666, 2006.
DOI : 10.1016/j.meatsci.2006.05.016

D. Bhandari, C. A. Murthy, and S. K. Pal, GENETIC ALGORITHM WITH ELITIST MODEL AND ITS CONVERGENCE, International Journal of Pattern Recognition and Artificial Intelligence, vol.10, issue.06, pp.731-747, 1996.
DOI : 10.1142/S0218001496000438

O. R. Bininda-emonds, M. Cardillo, K. E. Jones, R. D. Macphee, R. M. Beck et al., The delayed rise of present-day mammals, Nature, vol.1, issue.7135, pp.507-512, 2007.
DOI : 10.1038/nature05634

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and regression trees, 1984.

H. J. Bremermann, Optimization through Evolution and Recombination, Spartan Books, 1962.

R. Cerf, Une théorie asymptotique des algorithmes génétiques, Thèse de doctorat, p.153, 1994.

R. Cerf, The dynamics of mutation-selection algorithms with large population sizes. Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, pp.455-508, 1996.

R. Cerf, A new genetic algorithm, The Annals of Applied Probability, vol.6, issue.3, pp.778-817, 1996.
DOI : 10.1214/aoap/1034968228

J. J. Commandeur, Matching configurations, 1991.

K. R. Coombes, H. A. Fritsche, C. Clarke, J. Chen, K. A. Baggerly et al., Quality Control and Peak Finding for Proteomics Data Collected from Nipple Aspirate Fluid by Surface-Enhanced Laser Desorption and Ionization, Clinical Chemistry, vol.49, issue.10, pp.49-1615, 2003.
DOI : 10.1373/49.10.1615

C. R. Darwin, On the Origin of Species by means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life, 1859.

A. Davenport, E. P. Tsang, C. J. Wang, and K. Zhu, GENET : a connectionist architecture for solving constraint satisfaction problems by iterative improvement, Proceedings 12th National Conference for Artificial Intelligence (AAAI), pp.325-330, 1994.

L. Davis, Handbook of Genetic Algorithms, 1991.

T. E. Davis and J. C. Principe, A Markov Chain Framework for the Simple Genetic Algorithm, Evolutionary Computation, vol.34, issue.3, pp.269-288, 1993.
DOI : 10.1162/evco.1993.1.3.269

R. Dawkins, River out of Eden, Basic Books, 1995.

D. Jong and K. A. , An analysis of the behaviour of a class of genetic adaptive systems, Doctoral Dissertation, 1975.

D. Jong and K. A. , Genetic Algorithms Are NOT Function Optimizers, Parallel Problem-Solving from Nature, pp.3-13, 1992.
DOI : 10.1016/B978-0-08-094832-4.50006-4

G. Dijksterhuis and J. C. Gower, The interpretation of Generalized Procrustes Analysis and allied methods, Food Quality and Preference, vol.3, issue.2, pp.67-87, 1991.
DOI : 10.1016/0950-3293(91)90027-C

I. Dimatteo, C. R. Genovese, and R. E. Kass, Bayesian curve-fitting with free-knot splines, Biometrika, vol.88, issue.4, pp.1055-1071, 2001.
DOI : 10.1093/biomet/88.4.1055

M. Dorigo and G. Di-caro, The Ant Colony Optimization meta-heuristic New Ideas in Optimization, pp.11-32, 1999.

M. Dorigo, D. Caro, G. Gambardella, and L. M. , Ant Algorithms for Discrete Optimization, Artificial Life, vol.54, issue.1, pp.137-172, 1999.
DOI : 10.1007/BF01797237

R. Duda, P. Hart, and D. Strok, Pattern classification and scene analysis, 2001.

L. J. Eshelman, R. A. Caruana, and J. D. Schaffer, The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination, Foundations of Genetic Algorithms, pp.265-283, 1989.
DOI : 10.1016/B978-0-08-050684-5.50020-3

U. Faigle and W. Kern, Some Convergence Results for Probabilistic Tabu Search, ORSA Journal on Computing, vol.4, issue.1, pp.32-37, 1992.
DOI : 10.1287/ijoc.4.1.32

T. A. Feo and M. G. Resende, A probabilisstic heuristic for a computationally difficult set covering problem, Operations Research Letters, vol.29, pp.330-341, 1989.

T. A. Feo and M. G. Resende, Greedy Randomized Adaptive Search Procedures, Journal of Global Optimization, vol.68, issue.2, pp.109-133, 1995.
DOI : 10.1007/BF01096763

G. Figù, E. Andrieu, S. Letourneux, J. P. Biesse, and . Vidal, Near infrared spectroscopy and pattern recognition as screening methods for classification of commercial tobacco blends, Congrès de Chimiométrie Novembre & 1er Décembre, p.30, 2004.

T. C. Fogarty, Varying the probability of mutation in the genetic algorithm, Proceedings of the third Internation Conference on Genetic Algorithms, pp.104-109, 1989.

L. J. Fogel, Biotechnology : Concepts and Applications, 1963.

L. J. Fogel, A. J. Owens, and M. J. Walsh, Artifical Intelligence through Simulated Evolution, 1966.

D. B. Fogel, Evolutionary Computation, 1998.
DOI : 10.1002/9781119214403.ch10

S. Fortune, A sweepline algorithm for Voronoi diagrams, Algorithmica, vol.14, issue.1-4, pp.153-174, 1987.
DOI : 10.1007/BF01840357

A. S. Fraser, Simulation of genetic systems, Journal of Theoretical Biology, vol.2, issue.3, pp.329-346, 1962.
DOI : 10.1016/0022-5193(62)90036-X

M. I. Freidlin and A. D. Wentzell, Random perturbations of dynamical systems, 1984.

J. H. Friedman, A variable span scatterplot smoother, Laboratory for Computational Statistics, 1984.

M. Gendreau, An introduction to tabu search Handbook of metaheuristics, Kluwer's International Series, pp.37-54, 2003.

B. Gidas, Nonstationary Markov chains and convergence of the annealing algorithm, Journal of Statistical Physics, vol.21, issue.1-2, pp.73-131, 1985.
DOI : 10.1007/BF01007975

F. Glover, Future paths for integer programming and links to artificial intelligence. Computers and operations research, pp.533-549, 1986.

F. Glover, J. Hao, E. Lutton, and E. Ronald, A template for scatter search and path relinking, Artificial evolution, pp.3-51, 1998.
DOI : 10.1007/BFb0026589

F. Glover and S. Hanafi, Tabu search and finite convergence, Discrete Applied Mathematics, vol.119, issue.1-2, pp.3-36, 2002.
DOI : 10.1016/S0166-218X(01)00263-3

F. Glover, M. Laguna, and R. Marti, Scatter search and path relinking : advances and applications Handbook of metaheuristics, Kluwer's International Series, pp.1-35, 2003.

D. E. Goldberg, Optimal initial population size for binary-coded genetic algorithms, TCGA Report, vol.85001, 1985.

D. E. Goldberg, Sizing populations for seria and parallel genetic algorithms, Proceedings of the third International Conference on Genetic Algorithms, pp.70-79, 1989.

D. E. Goldberg, Genetic Algorithms in Search, Machine Learning, 1989.

D. E. Goldberg, K. Deb, and J. H. Clark, Genetic algorithms, noise and the sizing of populations, Complex Systems, vol.6, pp.333-362, 1992.

J. C. Gower, Generalized procrustes analysis, Psychometrika, vol.35, issue.1, pp.33-51, 1975.
DOI : 10.1007/BF02291478

B. F. Green, The orthogonal approximation of an oblique structure in factor analysis, Psychometrika, vol.5, issue.4, pp.429-440, 1952.
DOI : 10.1007/BF02288918

P. J. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

J. S. Gustafsson, A. Blomberg, and M. Rudemo, Warping two-dimensional electrophoresis gel images to correct for geometric distortions of the spot pattern, ELECTROPHORESIS, vol.23, issue.11, pp.1731-1744, 2002.
DOI : 10.1002/1522-2683(200206)23:11<1731::AID-ELPS1731>3.0.CO;2-#

W. J. Gutjahr, ACO algorithms with guaranteed convergence to the optimal solution, Information Processing Letters, vol.82, issue.3, pp.145-153, 2002.
DOI : 10.1016/S0020-0190(01)00258-7

S. Hanafi, On the convergence of Tabu Search, Journal of Heuristics, vol.7, issue.1, pp.47-58, 2001.
DOI : 10.1023/A:1026565712483

P. J. Hancock, An empirical comparison of selection methods in evolutionary algorithms, Ed.) Evolutionary Computing : AISB Workshop, pp.80-94, 1994.
DOI : 10.1007/3-540-58483-8_7

P. J. Hancock, Selection methods for evolutionary algorithms) Practical Handbook of Genetic Algorithms : New Frontiers, pp.67-92, 1996.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference and Prediction, 2001.

D. Henderson, S. H. Jacobson, and A. W. Johnson, The Theory and Practice of Simulated Annealing, Handbook of metaheuristics, Kluwer's International Series, pp.287-319, 2003.
DOI : 10.1007/0-306-48056-5_10

M. Hilario, A. Kalousis, M. Müller, and C. Pellegrini, Machine learning approaches to lung cancer prediction from mass spectra, PROTEOMICS, vol.3, issue.9, pp.1716-1719, 2003.
DOI : 10.1002/pmic.200300523

J. H. Holland, Adaptation in Natural and Artificial Systems, 1975.

J. R. Hurley and R. B. Cattell, The procrustes program: Producing direct rotation to test a hypothesized factor structure, Behavioral Science, vol.8, issue.2, pp.258-262, 1962.
DOI : 10.1002/bs.3830070216

P. James, Protein identification in the post-genome era: the rapid rise of proteomics, Quarterly Reviews of Biophysics, vol.30, issue.4, pp.279-331, 1997.
DOI : 10.1017/S0033583597003399

C. Z. Janikow and Z. Michalewicz, An experimental comparison of binary and floating point representations in genetic algorithms, Proceedings of the Fourth International Conference Genetic Algorithms, pp.31-36, 1991.

J. N. Jeffers, Two Case Studies in the Application of Principal Component Analysis, Applied Statistics, vol.16, issue.3, pp.225-236, 1967.
DOI : 10.2307/2985919

N. O. Jeffries, Performance of a genetic algorithm for mass spectrometry proteomics, BMC Bioinformatics, vol.5, issue.180, 2004.

K. Jong, E. Marchiori, and A. Van-der-vaart, Analysis of Proteomic Pattern Data for Cancer Detection, Lecture Notes in Computer Science, vol.3005, pp.41-51, 2004.
DOI : 10.1007/978-3-540-24653-4_5

K. Kaczmarek, B. Walczak, S. De-jong, and B. G. Vandeginste, Preprocessing of two-dimensional gel electrophoresis images, PROTEOMICS, vol.4, issue.8, pp.2377-2389, 2004.
DOI : 10.1002/pmic.200300758

J. Klose, Protein mapping by combined isoelectric focusing and electrophoresis : a two-dimensional technique, Humangenetik, vol.26, pp.231-234, 1975.

W. Kristof, A theorem on the trace of certain matrix products and some applications, Journal of Mathematical Psychology, vol.7, issue.3, pp.515-530, 1970.
DOI : 10.1016/0022-2496(70)90037-4

W. Kristof and B. Wingersky, Generalization of the orthogonal Procrustes rotation procedure for more than two matrices, Proceedings of the 79th annual convention of the American psychological association, pp.89-90, 1971.
DOI : 10.1037/e465422008-046

R. Le, M. Hino, and M. Ezzo, How antiquated statistics can overcome showy youth, Journal of Adequate Statistic Methods, vol.18, issue.5, 2005.

R. Leardi, Application of genetic algorithm-PLS for feature selection in spectral data sets, Journal of Chemometrics, vol.40, issue.5-6, pp.643-655, 2000.
DOI : 10.1002/1099-128X(200009/12)14:5/6<643::AID-CEM621>3.0.CO;2-E

K. R. Lee, X. Lin, D. C. Park, and S. Eslava, Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method, PROTEOMICS, vol.3, issue.9, pp.1680-1686, 2003.
DOI : 10.1002/pmic.200300515

H. Liu, J. Li, and L. Wong, A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns, Genome Informatics, vol.13, pp.51-60, 2002.

S. L. Lohr, Sampling : Design and Analysis, 1999.

M. Lundy and A. Mees, Convergence of an annealing algorithm, Mathematical Programming, pp.111-124, 1986.
DOI : 10.1007/BF01582166

E. Marengo, E. Robotti, V. Gianotti, P. G. Righetti, D. Cecconi et al., A new integrated statistical approach to the diagnostic use of two-dimensional maps, ELECTROPHORESIS, vol.24, issue.12, pp.225-236, 2003.
DOI : 10.1002/elps.200390019

M. D. Mckay, W. J. Conover, and R. J. Beckman, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, pp.239-245, 1979.

Z. Michalewicz, Genetic Algorithms + Data Structure = Evolution Programs, 1992.

D. Mitra, F. Romeo, and A. Sangiovanni-vincentelli, Convergence and finite-time behavior of simulated annealing, Advances in Applied Probability, vol.6, issue.03, pp.747-771, 1986.
DOI : 10.1126/science.220.4598.671

N. Mladenovic, A Variable neighborhood algorithm -a new metaheuristic for combinatorial optimization In : Abstracts of papers presented at Optimization Days, p.112, 1995.

N. Mladenovic and P. Hansen, Variable neighborhood search, Computers & Operations Research, vol.24, issue.11, pp.1097-1100, 1997.
DOI : 10.1016/S0305-0548(97)00031-2

URL : https://hal.archives-ouvertes.fr/hal-00979295

J. Monod, Le Hasard et la Nécessité Le seuil, p.155, 1970.

H. Mülenbein and D. Schlierkamp-voosen, The Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA), Evolutionary Computation, vol.2, issue.4, pp.335-360, 1994.
DOI : 10.1016/0301-6226(91)90060-4

F. Muri-majoube and B. Prum, Une approche statistique de l'analyse des génomes, La Gazette des Mathématiciens, vol.89, pp.63-98, 2002.

T. Naes and B. Mevik, Understanding the collinearity problem in regression and discriminant analysis, Journal of Chemometrics, vol.44, issue.4, pp.413-426, 2001.
DOI : 10.1002/cem.676

A. E. Nix and M. D. Vose, Modeling genetic algorithms with Markov chains, Annals of Mathematics and Artificial Intelligence, vol.5, issue.1, pp.79-88, 1992.
DOI : 10.1007/BF01530781

O. Farrell and P. H. , High resolution two-dimensional electrophoresis of proteins, Journal of Biological Chemistry, vol.250, pp.4007-4021, 1975.

C. C. Peck and A. P. Dhawan, Genetic Algorithms as Global Random Search Methods: An Alternative Perspective, Evolutionary Computation, vol.3, issue.2, pp.39-80, 1995.
DOI : 10.1007/BF01530781

T. Peng, M. Jian, and F. Zhiping, A stochastic tabu search strategy and its global convergence, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, pp.410-414, 1997.
DOI : 10.1109/ICSMC.1997.625784

E. F. Petricoin, A. M. Ardekani, B. A. Hitt, P. J. Levine, V. A. Fusaro et al., Use of proteomic patterns in serum to identify ovarian cancer, The Lancet, vol.359, issue.9306, pp.572-577, 2002.
DOI : 10.1016/S0140-6736(02)07746-2

J. Pittman, Adaptive Splines and Genetic Algorithms, Journal of Computational and Graphical Statistics, vol.11, issue.3, pp.615-638, 2002.
DOI : 10.1198/106186002448

F. A. Potra and X. Liu, Aligning Families of Two-Dimensional Gels by a Combined Multiresolution Forward-Inverse Transformation Approach, Journal of Computational Biology, vol.13, issue.7, pp.1384-1395, 2006.
DOI : 10.1089/cmb.2006.13.1384

J. Prados, A. Kalousis, J. Sanchez, L. Allard, O. Carrette et al., Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents, PROTEOMICS, vol.4, issue.8, pp.4-2320, 2004.
DOI : 10.1002/pmic.200400857

Y. Qu, B. Adam, Y. Yasui, M. D. Ward, L. H. Xazares et al., Boosted Decision Tree Analysis of Surface-enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles Discriminates Prostate Cancer from Noncancer Patients, Clinical Chemistry, issue.10, pp.48-1835, 2002.

R. Development and C. Team, R : A language and environment for statistical computing . R Foundation for Statistical Computing, 2004.

N. J. Radcliffe and P. D. Surry, For mae and the variance of fitness, Foundations of the Genetic Algorithms, pp.51-72, 1995.

I. Rechenberg, Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipen der biologischen Evolution, Frommann-Holzboog Verlag, 1973.

J. Rees and G. J. Koehler, An Investigation of GA Performance Results for Different Cardinality Alphabets, Evolutionary algorithms : IMA Volumes in Mathematics and its Applications, pp.191-206, 1999.
DOI : 10.1007/978-1-4612-1542-4_11

C. R. Reeves, Genetic algorithms and neighbourhood search, ) Evolutionary Computing : AISB Workshop, 1994.
DOI : 10.1007/3-540-58483-8_10

C. R. Reeves, A genetic algorithm for flowshop sequencing, Computers & Operations Research, vol.22, issue.1, pp.5-13, 1995.
DOI : 10.1016/0305-0548(93)E0014-K

C. R. Reeves, C. Fonlupt, J. K. Hao, E. Lutton, E. Ronald et al., Fitness Landscapes and Evolutionary Algorithms, Artificial Evolution : 4th European Conference, pp.3-20, 2000.
DOI : 10.1007/10721187_1

C. R. Reeves and J. E. Rowe, Genetic algorithms -Principles and perspectives, A guide to GA theory, 2003.

C. Reynès, S. De-souza, R. Sabatier, G. Figù-eres, and B. Vidal, Selection of discriminant wavelength intervals in NIR spectrometry with genetic algorithms, Journal of Chemometrics, vol.1, issue.3-4, pp.136-145, 2007.
DOI : 10.1002/cem.1000

C. Reynès, S. Roche, L. Tiers, R. Sabatier, P. Jouin et al., Comparison between surface and bead-based MALDI profiling technologies using a single bioinformatics algorithm, Clinical Proteomics, vol.7, issue.3-4, 2007.
DOI : 10.1007/BF02752497

M. Rogers, J. Graham, and R. P. Tonge, Statistical models of shape for the analysis of protein spots in two-dimensional electrophoresis gel images, PROTEOMICS, vol.3, issue.6, pp.887-896, 2003.
DOI : 10.1002/pmic.200300421

V. Rousson and T. Gasser, Some Case Studies of Simple Component Analysis, 2003.

V. Rousson and T. Gasser, Simple component analysis, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.26, issue.4, pp.539-555, 2004.
DOI : 10.1111/1467-9876.00204

G. Rudolph, Convergence properties of Evolutionary Algorithms, 1997.

J. Salmi, T. Aittokallio, J. Westerholm, M. Griese, A. Rosengren et al., Hierarchical grid transformation for image warping in the analysis of two-dimensional electrophoresis gels, PROTEOMICS, vol.2, issue.11, pp.1504-1515, 2002.
DOI : 10.1002/1615-9861(200211)2:11<1504::AID-PROT1504>3.0.CO;2-B

P. H. Schönemann and R. M. Carroll, Fitting one matrix to another under choice of a central dilation and a rigid motion, Psychometrika, vol.27, issue.2, pp.245-256, 1970.
DOI : 10.1007/BF02291266

H. Schwefel, Numerische Optimierung von Computer-modellen mittels der Evolutionsstrategie, Numerical Optimization of Computer Models, 1977.
DOI : 10.1007/978-3-0348-5927-1

J. L. Shapiro, A. Prügel-bennett, and M. Rattray, A statistical mechanical formulation of the dynamics of genetic algorithms, Lecture Notes in Computer Science, vol.865, pp.17-27, 1994.
DOI : 10.1007/3-540-58483-8_2

Z. Smilansky, Automatic registration for images of two-dimensional protein gels, ELECTROPHORESIS, vol.1, issue.9, pp.1616-1626, 2001.
DOI : 10.1002/1522-2683(200105)22:9<1616::AID-ELPS1616>3.0.CO;2-Z

J. M. Sorace and M. Zhan, A data review and re-assessment of ovarian cancer serum proteomic profiling, BMC Bioinformatics, pp.4-24, 2003.

P. F. Stadler and G. P. Wagner, Algebraic Theory of Recombination Spaces, Evolutionary Computation, vol.42, issue.3, pp.241-275, 1998.
DOI : 10.1109/4235.585893

W. J. Stewart, Introduction to the numerical solution of Markov Chains, 1995.

T. Berge and J. M. , Orthogonal procrustes rotation for two or more matrices, Psychometrika, vol.42, issue.2, pp.267-276, 1977.
DOI : 10.1007/BF02294053

E. V. Thomas, A Primer on Multivariate Calibration, Analytical Chemistry, vol.66, issue.15, pp.795-804, 1994.
DOI : 10.1021/ac00087a722

R. Tibshirani, T. Hastie, B. Narasimhan, S. Soltys, G. Shi et al., Sample classification from protein mass spectrometry, by 'peak probability contrasts', Bioinformatics, vol.20, issue.17, pp.20-3034, 2004.
DOI : 10.1093/bioinformatics/bth357

E. P. Tsang, C. J. Wang, A. Davenport, C. Voudouris, and T. L. Lau, A family of stochastic methods for constraint satisfaction and optimisation, Proceedings of the First International Conference on The Practical Application of Constraint Technologies and Logic Programming (PACLP), pp.359-383, 1999.

S. Veeser, M. J. Dunn, and G. Z. Yang, Multiresolution image registration for two-dimensional gel electrophoresis, PROTEOMICS, vol.1, issue.7, pp.856-870, 2001.
DOI : 10.1002/1615-9861(200107)1:7<856::AID-PROT856>3.0.CO;2-R

S. K. Vines, Simple principal components, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.49, issue.4, pp.441-451, 2000.
DOI : 10.1111/1467-9876.00204

M. D. Vose, Generalizing the notion of schema in genetic algorithms, Artificial Intelligence, vol.50, issue.3, pp.385-396, 1988.
DOI : 10.1016/0004-3702(91)90019-G

T. Voss and P. Haberl, Observations on the reproducibility and matching efficiency of two-dimensional electrophoresis gels: Consequences for comprehensive data analysis, Electrophoresis, vol.18, issue.16, pp.3345-3350, 2000.
DOI : 10.1002/1522-2683(20001001)21:16<3345::AID-ELPS3345>3.0.CO;2-Z

M. Wagner, D. Naik, and A. Pothen, Protocols for disease classification from mass spectrometry data, PROTEOMICS, vol.3, issue.9, pp.1692-1698, 2003.
DOI : 10.1002/pmic.200300519

X. Wang and D. D. Feng, HYBRID REGISTRATION FOR TWO-DIMENSIONAL GEL PROTEIN IMAGES, Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, pp.17-21, 2005.
DOI : 10.1142/9781860947322_0020

J. Watson and F. Crick, Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid, Nature, vol.9, issue.4356, pp.737-738, 1953.
DOI : 10.1016/0006-3002(53)90232-7

D. Whitney, The GENITOR algorithm and selection pressure : Why ranked-based allocation of reproductive trials is best, Proceedings of the 3rd International Conference on Genetic Algorithms, pp.116-121, 1989.

D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, vol.1, issue.1, pp.67-82, 1997.
DOI : 10.1109/4235.585893

T. Wu, C. Lin, and R. C. Weng, Probability estimates for multi-class classification by pairwise coupling, Journal of Machine Learning Research, vol.5, pp.975-1005, 2004.

Y. Yasui, M. S. Pepe, M. L. Thompson, B. Adam, G. L. Wright et al., A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection, Biostatistics, vol.4, issue.3, pp.449-463, 2003.
DOI : 10.1093/biostatistics/4.3.449