.. Méthodes-de-recherche-dans-des-arbres-ou-des-graphes........, 33 2.3.1 Principe de la recherche dans des arbres ou des graphes, p.36

.. Principes-d-'inférence, 72 3.4.1 Choix de représentation, p.79

.. Cadre-méthodologique-de-la-sélection-de-modèle, 80 3.5.1 Estimation empirique de l'erreur de généralisation, p.85

L. Réduction-de, 86 3.6.1 Sélection de variables pertinentes, p.88

.. Estimation-du-ventàventà-partir-des-trajectoires-radar......, Formulation duprobì eme d'extraction de vent, p.166

.. Le-modèlè-a-´-energie-totale, .. La-masse, and . .. Mach, 171 5.5.3 La problématique de la prédiction par les systèmes 175 5.5.5 L'apprentissage appliquéappliquéà la prévision de trajectoires d'avions

. Cf, propriétés de convergence du maximum de vraisemblance. [1] Study of the acquisition of data from aircraft operators to aid trajectory prediction calculation , tech. rep., EUROCONTROL Experimental Center, p.170, 1998.

U. Ahlstrom, Subjective workload ratings and eye movement activity measures, technical report DOT/FAA/CT-05/32, Federal Aviation Administration William J. Hughes Technical Center, p.139, 2005.

H. Akaike, Information theory and an extension of the maximum likelihood principle, Selected Papers of Hirotugu Akaike, pp.199-213, 1998.

R. Alligier, Apprentissage artificiel appliquéappliquéà la prévision de trajectoire d'avion, pp.179-189, 0192.

R. Alligier, D. Gianazza, and N. Durand, Energy Rate Prediction Using an Equivalent Thrust Setting Profile (regular paper, International Conference on Research in Air Transportation (ICRAT), p.158

R. Alligier, D. Gianazza, and N. Durand, Ground-based estimation of aircraft mass, adaptive vs. least squares method, Proceedings of the 10th USA, pp.154-157, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00911686

R. Alligier, D. Gianazza, and N. Durand, Learning the aircraft mass and thrust to improve the ground-based trajectory prediction of climbing flights, Transportation Research Part C : Emerging Technologies, pp.154-158, 2013.

R. Alligier, D. Gianazza, and N. Durand, Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction, IEEE Transactions on Intelligent Transportation Systems, vol.16, issue.6, pp.1-12, 2015.
DOI : 10.1109/TITS.2015.2437452

URL : https://hal.archives-ouvertes.fr/hal-01181173

R. Alligier, D. Gianazza, and N. Durand, Machine learning applied to airspeed prediction during climb, Proceedings of the 11th USA, pp.154-160, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01168664

R. Alligier, D. Gianazza, M. G. Hamed, and N. Durand, Comparison of Two Ground-based Mass Estimation Methods on Real Data (regular paper, International Conference on Research in Air Transportation (ICRAT), pp.154-179, 2014.

C. Allignol, N. Barnier, N. Durand, and J. Alliot, A new framework for solving en-routes conflicts, ATM 2013, pp.1-52, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00828736

J. Alliot, S. Aupetit, S. B. Hamida, I. Boussaid, G. Caporossi et al., Métaheuristiques : Recuits simulé, recherche avec tabous, recherchè a voisinages variables, méthodes GRASP, algorithmesévolutionnairesrithmesévolutionnaires , fourmis artificielles, pp.32-47, 2014.

J. Alliot, H. Gruber, and M. Schoenauer, Using genetic algorithms for solving ATC conflicts, Proceedings of the Ninth Conference on Artificial Intelligence Application, p.170, 1993.

J. Alliot, N. Durand, D. Gianazza, and J. Gotteland, Finding and proving the optimum : Cooperative stochastic and deterministic search, Proceedings of the European Conference on Artificial Intelligence, pp.2012-2040, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00938712

J. Alliot, N. Durand, D. Gianazza, and J. Gotteland, Implementing an interval computation library for ocaml on x86, International Conference on Functional Programming, pp.28-51
URL : https://hal.archives-ouvertes.fr/hal-00934812

L. Armijo, Minimization of functions having Lipschitz continuous first partial derivatives, Pacific Journal of Mathematics, vol.16, issue.1, pp.1-3, 1966.
DOI : 10.2140/pjm.1966.16.1

S. Athènes, P. Averty, S. Puechmorel, D. Delahaye, and C. Collet, Atc complexity and controller workload : Trying to bridge the gap, Proceedings of the International Conference on HCI in Aeronautics, pp.56-60, 2002.

Y. Bengio, A. Courville, and P. Vincent, Representation learning : A review and new perspectives, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.35, pp.1798-1828, 2013.

H. Beyer and H. Schwefel, Evolution strategies?a comprehensive introduction, Natural Computing, vol.1, issue.1, pp.3-52, 2002.
DOI : 10.1023/A:1015059928466

C. M. Bishop, Neural networks for pattern recognition, p.141, 1996.

C. Blum, J. Puchinger, G. R. Raidl, and A. Roli, Hybrid metaheuristics in combinatorial optimization: A survey, Applied Soft Computing, vol.11, issue.6, pp.4135-4151, 2011.
DOI : 10.1016/j.asoc.2011.02.032

URL : https://hal.archives-ouvertes.fr/hal-01224683

C. Blum, A. Roli, and M. Sampels, Hybrid metaheuristics : an emerging approach to optimization, p.51, 2008.

C. Blum and K. Socha, Training feed-forward neural networks with ant colony optimization: an application to pattern classification, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), p.118, 2005.
DOI : 10.1109/ICHIS.2005.104

O. Bousquet, S. Boucheron, and G. Lugosi, Introduction to Statistical Learning Theory, Advanced Lectures on Machine Learning, pp.169-207, 2004.
DOI : 10.1007/3-540-45435-7_5

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and regression trees, pp.88-120, 1984.

S. Cafieri, P. Brisset, and N. Durand, A mixed-integer optimization model for air traffic deconfliction, Proceedings of the Toulouse Global Optimization Workshop, pp.27-30, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00938717

G. Chaloulos, E. Crück, and J. Lygeros, A simulation based study of subliminal control for air traffic management, Transportation Research Part C: Emerging Technologies, vol.18, issue.6, pp.963-974, 2010.
DOI : 10.1016/j.trc.2010.03.002

G. Chatterji and B. Sridhar, Measures for air traffic controller workload prediction, 1st AIAA, Aircraft, Technology Integration, and Operations Forum, p.139, 2001.
DOI : 10.2514/6.2001-5242

G. B. Chatterji and B. Sridhar, Neural network based air traffic controller workload prediction, American Control Conference Proceedings of the 1999, pp.2620-2624, 1999.

H. Chernoff, On the distribution of the likelihood ratio, The Annals of Mathematical Statistics, pp.573-578, 1954.

M. Clerc and J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, Evolutionary Computation, IEEE Transactions on, vol.6, pp.58-73, 2002.

C. A. Coello and A. Carlos, A survey of constraint handling techniques used with evolutionary algorithms, Lania-RI-99-04, Laboratorio Nacional de Informática Avanzada, p.50, 1999.

S. Consortium, Milestone Deliverable D3 : The ATM Target Concept, tech. rep, pp.24-169, 2007.

R. A. Coppenbarger, Climb trajectory prediction enhancement using airline flightplanning information, AIAA Guidance, Navigation, and Control Conference, p.170, 1999.

A. Cornuéjols and L. Miclet, Apprentissage artificiel : concepts et algorithmes, Editions Eyrolles, pp.58-74, 2011.

D. Haan and A. Stoffelen, Assimilation of high-resolution Mode-S wind and temperature observations in a regional NWP model for nowcasting applications, Weather and Forecasting, pp.918-937, 2012.

D. Delahaye and S. Puechmorel, TAS and wind estimation from radar data, 2009 IEEE/AIAA 28th Digital Avionics Systems Conference, pp.2-163, 2009.
DOI : 10.1109/DASC.2009.5347547

URL : https://hal.archives-ouvertes.fr/hal-00938201

J. E. Dennis, J. , and J. J. Moré, Quasi-Newton Methods, Motivation and Theory, SIAM Review, vol.19, issue.1, pp.46-89, 1977.
DOI : 10.1137/1019005

URL : https://hal.archives-ouvertes.fr/hal-01495720

D. J. Diston, Computational Modelling and Simulation of Aircraft and the Environment : Volume 1-Platform Kinematics and Synthetic Environment, p.229, 2009.
DOI : 10.1002/9780470744130

J. Dréo, A. Pétrowski, P. Siarry, and E. Taillard, Métaheuristiques pour l'optimisation difficile, Eyrolles, pp.2-212, 2003.

R. A. Dunne and N. A. Campbell, On the pairing of the softmax activation and cross-entropy penalty functions and the derivation of the softmax activation function, Proceedings of the Eighth Australasian Conference on Neural Networks, p.141, 1997.

N. Durand, Optimisation de trajectoires pour la résolution de conflits aériens en route, p.170, 1996.

N. Durand and J. Alliot, Un algorithme de colonie de fourmis pour résoudre des conflits aériens, ROADEF, p.52, 2010.

N. Durand, J. Alliot, and G. Granger, A statistical analysis of the influence of vertical and ground speed errors on conflict probe, Proceedings of ATM2001, p.163, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00938005

N. Durand, J. Alliot, and J. Noailles, Automatic aircraft conflict resolution using genetic algorithms, Proceedings of the 1996 ACM symposium on Applied Computing , SAC '96, p.52, 1996.
DOI : 10.1145/331119.331195

URL : https://hal.archives-ouvertes.fr/hal-00937685

N. Durand and J. Alliot, Ant colony optimization for air traffic conflict resolution , in 8th USA/Europe Air Traffic Management Research and Developpment Seminar, pp.52-170, 2009.

R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp.39-43, 1995.
DOI : 10.1109/MHS.1995.494215

A. Edelman and H. Murakami, Polynomial roots from companion matrix eigenvalues, Mathematics of Computation, vol.64, issue.210, pp.763-776, 1995.
DOI : 10.1090/S0025-5718-1995-1262279-2

A. Eiben and J. Smith, Introduction to Evolutionary Computing, pp.47-132, 2003.

Y. L. Fablec, Prévision de trajectoires d'avions par réseaux de neurones, p.171, 1999.

G. Flynn, A. Benkouar, and R. Christien, Adaptation of workload model by optimisation algorithms, tech. rep., Eurocontrol, p.138, 2005.

J. Fox, Applied regression analysis, linear models, and related methods, pp.92-99, 1997.

J. H. Friedman, Greedy function approximation : a gradient boosting machine, Annals of statistics, pp.1189-1232, 2001.

J. H. Friedman, Stochastic gradient boosting, Computational Statistics & Data Analysis, vol.38, issue.4, pp.367-378, 2002.
DOI : 10.1016/S0167-9473(01)00065-2

M. G. Hamed, Méthodes non-paramétriques pour la prévision d'intervalles avec haut niveau de confiance : applicationàapplicationà la prévision de trajectoires d'avions, Thèse doctorat informatique de l'INPT, p.171, 2014.

D. Gianazza, pour la s??paration en 3D des flux de trafic a??rien, Journal Europ??en des Syst??mes Automatis??s, vol.38, issue.9-10, pp.1065-1095, 2004.
DOI : 10.3166/jesa.38.1065-1095

D. Gianazza, Airspace configuration using air traffic complexity metricsEurope Seminar on Air Traffic Management Research and Development , 2007. best paper of " Dynamic Airspace Configuration " track, Proceedings of the 7 th USA, pp.126-138

D. Gianazza, Smoothed traffic complexity metrics for airspace configuration schedules, Proceedings of the 3nd International Conference on Research in Air Transportation, ICRAT, pp.126-137, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01020711

D. Gianazza, Forecasting workload and airspace configuration with neural networks and tree search methods, Artificial Intelligence, vol.174, issue.7-8, pp.530-549, 2010.
DOI : 10.1016/j.artint.2010.03.001

URL : https://hal.archives-ouvertes.fr/hal-01020725

D. Gianazza, C. Allignol, and N. Saporito, An efficient airspace configuration forecast, Proceedings of the 8th USA, pp.126-144, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01020720

D. Gianazza and J. M. Alliot, Optimization of air traffic control sector configurations using tree search methods and genetic algorithms, Proceedings. The 21st Digital Avionics Systems Conference, pp.126-131, 2002.
DOI : 10.1109/DASC.2002.1067912

URL : https://hal.archives-ouvertes.fr/hal-00990194

D. Gianazza, J. M. Alliot, and G. Granger, Optimal combinations of air traffic control sectors using classical and stochastic methods, Proceedings of the 2002 International Conference on Artificial Intelligence, pp.126-131, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00990320

D. Gianazza and N. Durand, Separating air traffic flows by allocating 3D-trajectories, The 23rd Digital Avionics Systems Conference (IEEE Cat. No.04CH37576), p.51, 2004.
DOI : 10.1109/DASC.2004.1391275

URL : https://hal.archives-ouvertes.fr/hal-00938063

D. Gianazza and N. Durand, Assessment of the 3d-separation of air traffic flows, Proceedings of the 6 th USA/Europe Seminar on Air Traffic Management Research and Development, p.51, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00938079

D. Gianazza, N. Durand, and N. Archambault, Allocating 3d-trajectories to air traffic flows, using a * and genetic algorithms, Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, p.51, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01020102

D. Gianazza and K. Guittet, Evaluation of air traffic complexity metrics using neural networks and sector status, Proceedings of the 2nd International Conference on Research in Air Transportation, ICRAT, pp.126-141, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00938105

D. Gianazza and K. Guittet, Selection and Evaluation of Air Traffic Complexity Metrics, 2006 ieee/aiaa 25TH Digital Avionics Systems Conference, pp.126-137, 2006.
DOI : 10.1109/DASC.2006.313710

URL : https://hal.archives-ouvertes.fr/hal-00938180

D. Gianazza and K. Guittet, Réseaux de neurones appliqués aux indicateurs de complexité et aux regroupements de secteurs aériens, pp.6-517, 2006.

J. C. Gilbert and C. Lemaréchal, Some numerical experiments with variablestorage quasi-newton algorithms, Mathematical programming, pp.407-435, 1989.

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

F. Glover, Tabu search : A tutorial, Interfaces, pp.20-74, 1990.

D. Goldberg, Genetic Algorithms, p.132, 1989.

D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, pp.46-47, 1989.

V. Gudise and G. Venayagamoorthy, Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), p.118, 2003.
DOI : 10.1109/SIS.2003.1202255

K. Guittet and D. Gianazza, Analyse descriptive des indicateurs de complexité du trafic aérienaérienà partir des données, IMAGE et COURAGE, p.126, 2005.

E. Hansen and G. W. Walster, Global optimization using interval analysis : revised and expanded, pp.39-51, 2003.

E. R. Hansen and R. I. Greenberg, An interval Newton method, Applied Mathematics and Computation, vol.12, issue.2-3, pp.89-98, 1983.
DOI : 10.1016/0096-3003(83)90001-2

P. E. Hart, N. J. Nilsson, and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, Systems Science and Cybernetics, IEEE Transactions on, vol.4, pp.100-107, 1968.

P. E. Hart, N. J. Nilsson, and B. Raphael, <u>Correction</u> to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths", ACM SIGART Bulletin, issue.37, pp.28-29, 1972.
DOI : 10.1145/1056777.1056779

T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning, pp.72-79, 2001.

B. Hilburn, Cognitive complexity in air traffic control, a litterature review, tech. rep., Eurocontrol experimental centre, pp.18-139, 2004.

S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber, Gradient flow in recurrent nets : the difficulty of learning long-term dependencies, p.118, 2001.

J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan press, pp.46-47, 1975.

W. Hollister, E. Bradford, and J. Welch, Using aircraft radar tracks to estimate wind aloft, pp.555-565, 1989.

R. Horst and H. Tuy, Global optimization : Deterministic approaches, p.32, 1996.

C. Hurter, R. Alligier, D. Gianazza, S. Puechmorel, G. Andrienko et al., Wind parameters extraction from aircraft trajectories, Computers, Environment and Urban Systems, pp.28-43, 2014.

C. Hurter, S. Conversy, D. Gianazza, and A. Telea, Interactive image-based information visualization for aircraft trajectory analysis, Transportation Research Part C : Emerging Technologies, pp.154-156, 2014.

L. Jourdan, M. Basseur, and E. Talbi, Hybridizing exact methods and metaheuristics: A taxonomy, European Journal of Operational Research, vol.199, issue.3, pp.620-629, 2009.
DOI : 10.1016/j.ejor.2007.07.035

URL : https://hal.archives-ouvertes.fr/inria-00484922

J. Kennedy, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.760-766, 2010.
DOI : 10.1109/ICNN.1995.488968

S. Kirkpatrick, C. Gelatt, and M. Vecchi, Optimization by simulated annealing, Science, pp.220-671, 1983.

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Ijcai, pp.1137-1145, 1995.

P. Kopardekar, Dynamic density : A review of proposed variables, FAA WJHTC internal document. overall conclusions and recommendations, Federal Aviation Administration, p.139, 2000.

P. Kopardekar and S. Magyarits, Measurement and prediction of dynamic density, Proceedings of the 5th USA, p.139, 2003.

J. K. Kuchar and L. C. Yang, A review of conflict detection and resolution modeling methods, Intelligent Transportation Systems, IEEE Transactions on, vol.1, pp.179-189, 2000.

S. Kullback and R. A. Leibler, On information and sufficiency, The annals of mathematical statistics, pp.79-86, 1951.

I. V. Laudeman, S. G. Shelden, R. Branstrom, and C. L. Brasil, Dynamic density : An air traffic management metric, tech. rep, p.139, 1999.

E. Lebarbier and T. Mary-huard, Le critère bic : fondements théoriques et interprétation . rapport de recherche n 5315, p.224, 2004.

F. Leung, H. Lam, S. Ling, and P. Tam, Tuning of the structure and parameters of a neural network using an improved genetic algorithm, IEEE Transactions on Neural Networks, vol.14, issue.1, pp.79-88, 2003.
DOI : 10.1109/TNN.2002.804317

D. C. Liu and J. Nocedal, On the limited memory bfgs method for large scale optimization , Mathematical programming, pp.503-528, 1989.

I. Lymperopoulos and J. Lygeros, Sequential Monte Carlo methods for multi-aircraft trajectory prediction in air traffic management, International Journal of Adaptive Control and Signal Processing, vol.22, issue.2, pp.830-849, 2010.
DOI : 10.1002/acs.1174

I. Lymperopoulos, J. Lygeros, and A. L. Visintini, Model Based Aircraft Trajectory Prediction During Takeoff, AIAA Guidance, Navigation, and Control Conference and Exhibit, p.171, 2006.
DOI : 10.2514/6.2006-6098

A. Majumdar and W. Y. Ochieng, Factors affecting air traffic controller workload : Multivariate analysis based on simulation modeling of controller workload, Transportation Research Record, Journal of the Transportation Research Board, pp.1788-58, 2002.

C. A. Manning, S. H. Mills, C. M. Fox, E. M. Pfleiderer, and H. J. Mogilka, Using air traffic control taskload measures and communication events to predict subjective workload, tech. rep., DTIC Document, p.139, 2002.

A. J. Masalonis, M. B. Callaham, and C. R. Wanke, Dynamic density and complexity metrics for realtime traffic flow management, Proceedings of the 5th USA, p.139, 2003.

P. Massart, Concentration inequalities and model selection, p.222, 2007.

W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, The bulletin of mathematical biophysics, pp.115-133, 1943.

Z. Michalewicz, Genetic algorithms+data structures=evolution programs, p.131, 1992.
DOI : 10.1007/978-3-662-02830-8

Z. Michalewicz, D. Dasgupta, R. G. Le-riche, and M. Schoenauer, Evolutionary algorithms for constrained engineering problems, Computers & Industrial Engineering, vol.30, issue.4, pp.851-870, 1996.
DOI : 10.1016/0360-8352(96)00037-X

M. Minoux, Programmation mathématique : théorie et algorithmes, pp.32-46, 1983.

M. L. Minsky and S. A. Papert, Perceptrons -Expanded Edition : An Introduction to Computational Geometry, pp.95-101, 1987.

R. Mogford, J. A. Guttman, S. L. Morrow, and P. Kopardekar, The complexity construct in air traffic control : A review and synthesis of the literature, tech. rep., FAA Technical Center : Atlantic City, pp.18-139, 1995.

R. E. Moore, Interval Analysis, p.179, 1966.

J. J. Moré and D. J. Thuente, Line search algorithms with guaranteed sufficient decrease, ACM Transactions on Mathematical Software, vol.20, issue.3, pp.286-307, 1994.
DOI : 10.1145/192115.192132

J. Nocedal, S. Wright, and L. , Numerical Optimization, Springer series in operations research and financial engineering, pp.31-44, 2006.

A. Nuic, User manual for base of aircraft data (bada) rev.3.9, tech. rep., EUROCON- TROL, avril 2011, pp.251-277

A. Nuic, User manual for base of aircraft data (bada) revision 3.10, tech. rep., EURO- CONTROL, avril 2012, pp.173-196

L. Pallottino, E. M. Feron, and A. Bicchi, Conflict resolution problems for air traffic management systems solved with mixed integer programming, Intelligent Transportation Systems, IEEE Transactions on, vol.3, pp.3-11, 2002.

D. Poles, Revision of atmosphere model in bada aircraft performance model, tech. rep, p.241, 2010.

R. Poli, J. Kennedy, and T. Blackwell, Particle swarm optimization, Swarm intelligence, pp.33-57, 2007.

X. Prats, V. Puig, J. Quevedo, and F. Nejjari, Multi-objective optimisation for aircraft departure trajectories minimising noise annoyance, Transportation Research Part C: Emerging Technologies, vol.18, issue.6, pp.975-989, 2010.
DOI : 10.1016/j.trc.2010.03.001

K. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution, p.50, 2006.
DOI : 10.1007/978-3-642-30504-7_8

B. D. Ripley, Pattern recognition and neural networks, p.224, 1996.
DOI : 10.1017/CBO9780511812651

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological review, pp.386-95, 1958.
DOI : 10.1037/h0042519

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal representations by error propagation, tech. rep, p.117, 1985.

S. Russell and P. Norvig, Artificial intelligence : A modern approach author, pp.33-42, 2009.

N. Saporito, C. Hurter, D. Gianazza, and G. Beboux, A participatory design for the visualization of airspace configuration forecasts, Proceedings of the 4th International Conference on Research in Air Transportation, pp.126-144, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00938480

M. Schoenauer and Z. Michalewicz, Evolutionary algorithms for constrained parameter optimization problems, Evolutionary Computation, p.50, 1996.

M. Schoenauer and Z. Michalewicz, Boundary operators for constrained parameter optimization problems, Proceedings of the Seventh International Conference on Genetic Algorithms, p.50, 1997.

M. Schoenauer and Z. Michalewicz, Sphere operators and their applicability for constrained parameter optimization problems, in Evolutionary Programming VII, p.50, 1998.

C. Schultz, D. Thipphavong, and H. Erzberger, Adaptive Trajectory Prediction Algorithm for Climbing Flights, AIAA Guidance, Navigation, and Control Conference, pp.157-196, 2012.
DOI : 10.2514/6.2012-4931

G. Schwarz, Estimating the dimension of a model, The annals of statistics, pp.461-464, 1978.

S. G. Self and K. Liang, Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions, Journal of the American Statistical Association, vol.72, issue.398, pp.605-610, 1987.
DOI : 10.1080/01621459.1987.10478472

Y. Shi and R. Eberhart, A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp.69-73, 1998.
DOI : 10.1109/ICEC.1998.699146

G. L. Slater, Adaptive improvement of aircraft climb performance for air traffic control applications, Proceedings of the IEEE Internatinal Symposium on Intelligent Control, p.171, 2002.
DOI : 10.1109/ISIC.2002.1157831

A. Slowik and M. Bialko, Training of artificial neural networks using differential evolution algorithm, 2008 Conference on Human System Interactions, p.118, 2008.
DOI : 10.1109/HSI.2008.4581409

B. Sridhar, K. S. Sheth, and S. Grabbe, Airspace complexity and its application in air traffic management, Proceedings of the 2nd USA/Europe Air trafic Management R&D Seminar, p.139

M. Stone, An asymptotic equivalence of choice of model by cross-validation and akaike's criterion, Journal of the Royal Statistical Society. Series B (Methodological), pp.44-47, 1977.

R. Storn and K. Price, Differential evolution?a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, vol.11, issue.4, pp.341-359, 1997.
DOI : 10.1023/A:1008202821328

H. Swenson, R. Barhydt, and M. Landis, Next Generation Air Transportation System (NGATS) Air Traffic Management (ATM)-Airspace Project, tech. rep., National Aeronautics and Space Administration, pp.24-169, 2006.

E. Talbi, Metaheuristics : from design to implementation, pp.32-47, 2009.
DOI : 10.1002/9780470496916

URL : https://hal.archives-ouvertes.fr/hal-00750681

K. Tastambekov, S. Puechmorel, D. Delahaye, and C. Rabut, Aircraft trajectory forecasting using local functional regression in sobolev space, Transportation Research Part C : Emerging Technologies, pp.1-22, 2014.

J. Taylor and M. Plumbley, Information Theory and Neural Networks, p.141, 1993.
DOI : 10.1016/S0924-6509(08)70042-4

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

M. E. Tipping, Bayesian Inference: An Introduction to Principles and Practice in Machine Learning, Advanced lectures on machine Learning, pp.41-62, 2004.
DOI : 10.1162/neco.1992.4.3.415

I. C. Trelea, The particle swarm optimization algorithm : convergence analysis and parameter selection, Information processing letters, pp.317-325, 2003.

P. Van-hentenryck, Numerica : a modeling language for global optimization, Proceedings of the Fifteenth international joint conference on Artifical intelligence, pp.1642-1647, 1997.

C. Vanaret, Hybridation d'algorithmesévolutionnairesalgorithmesévolutionnaires et de méthodes d'intervalles pour l'optimisation deprobì emes difficiles, Thèse doctorat informatique de l'INPT, pp.51-55, 2015.

C. Vanaret, D. Gianazza, N. Durand, and J. Gotteland, Benchmarking conflict resolution algorithms (regular paper), International Conference on Research in Air Transportation (ICRAT), pp.28-52

C. Vanaret, D. Gianazza, J. Gotteland, and N. Durand, Résolution de conflits aériens par un algorithmè a ´ evolution différentielle, tech. rep., 13e congrès annuel de la Société française de Recherche Opérationnelle et d'Aidè a la Décision, pp.2012-2040, 2012.

C. Vanaret, J. Gotteland, and N. Durand, Hybridation de programmation par contraintes sur intervalles et d'algorithmesévolutionnairesalgorithmesévolutionnaires pour l'optimisation globalecooperation of evolutionary algorithms and interval constraint programming, ROA- DEF 2013, 14ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aidè a la Décision, p.51, 2015.

V. N. Vapnik, An overview of statistical learning theory, IEEE Transactions on Neural Networks, vol.10, issue.5, pp.988-999, 1999.
DOI : 10.1109/72.788640

S. A. Vavasis, Complexity issues in global optimization : a survey, in Handbook of global optimization, pp.27-41, 1995.

C. Verlhac and S. Manchon, Optimization of opening schemes, Proceedings of the fourth USA, p.127, 2001.

A. Warren, Trajectory prediction concepts for next generation air traffic management, Europe ATM R&D Seminar, p.171, 2000.

A. Warren and Y. Ebrahimi, Vertical path trajectory prediction for next generation atm Proceedings., 17th DASC. The AIAA, Digital Avionics Systems Conference, pp.11-12, 1998.

A. Webb, D. Lowe, and M. D. Bedworth, A comparison of nonlinear optimisation strategies for feed-forward adaptive layered networks, tech. rep., DTIC Document, p.117, 1988.

J. D. Welch, J. W. Andrews, B. D. Martin, and B. Sridhar, Macroscopic workload model for estimating en route sector capacity, Proc. of 7th USA/Europe ATM Research and Development Seminar, p.138, 2007.

P. Wolfe, Convergence conditions for ascent methods, SIAM review, pp.226-235, 1969.

D. H. Wolpert, The Supervised Learning No-Free-Lunch Theorems, Soft Computing and Industry, pp.25-42, 2002.
DOI : 10.1007/978-1-4471-0123-9_3