Team tao

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Other Grants and Activities
Dissemination
Bibliography

Bibliography

Publications of the year

Books and Monographs

[1]
P. Bourgine, F. Képès, M. Schoenauer (editors)
Proceedings of the First European Conference on Complex Systems – ECCS'05, 2005.

Doctoral dissertations and Habilitation theses

[2]
N. Godzik.
Une approche évolutionnaire de la robotique modulaire et anticipative, Ph. D. Thesis, Université de Paris Sud – Orsay, 2005.
[3]
J. Maloberti.
Improving Inductive Logic Programming with Constraint Satisfaction Techniques: Applications to Frequent Query Discovery, Ph. D. Thesis, Université de Paris Sud – Orsay, 2005.
[4]
J. Mary.
Étude de l'apprentissage actif. Application à la conduite d'expériences, Ph. D. Thesis, Université de Paris Sud – Orsay, 2005.

Articles in refereed journals and book chapters

[5]
A. Auger.
Convergence results for (1, $ \lambda$ )-SA-ES using the theory of $ \varphi$ -irreducible Markov chains, in: Theoretical Computer Science, 2005, no 334(1-3), p. 35–69.
[6]
J. Bernauer, A. Poupon, J. Azé, J. Janin.
A docking analysis of the statistical physics of protein-protein recognition, in: Journal Physical Biology, 2005.
[7]
C. Germain, V. Breton, P. Clarysse, Y. Gaudeau, T. Glatard, E. Jeannot, Y. Legré, C. Loomis, J. Montagnat, J.-M. Moureaux, A. Osorio, X. Pennec, R. Texier.
Grid-enabling medical image analysis, in: Journal of Clinical Monitoring and Computing, Extended version of the BioGrid 2005 paper, 2005, vol. 19, no 4-5, p. 339-349
http://dx.doi.org/10.1007/s10877-005-0679-9.
[8]
C. Germain, R. Texier, A. Osorio.
Exploration of Medical Images on the Grid, in: Methods of Information in Medecine, 2005, vol. 44, no 2, p. 227-232.
[9]
P. Morel, H. Hamda, M. Schoenauer.
Computational Chair Design using Genetic Algorithms, 2005, vol. 71, no 3, p. 95-99.
[10]
A. Selikhov, C. Germain.
A channel memory based environment for MPI applications, in: Future Generation Computer Systems, 2005, vol. 21, no 5, p. 709-715
http://dx.doi.org/10.1016/j.future.2004.05.011.

Publications in Conferences and Workshops

[11]
E. Alphonse, A. Amrani, J. Azé, A.-D. M. Thomas Heitz, M. Roche.
Préparation des données et analyse des résultats de DEFT'05, in: DEFT'05, 2005.
[12]
A. Auger, M. Schoenauer, O. Teytaud.
Local and global oder 3/2 convergence of a surrogate evolutionary algorithm, in: GECCO - genetic and evolutionary computation conference, Washington, 2005, p. 857-864
http://hal.inria.fr/inria-00000540/en/.
[13]
J. Azé, M. Roche, Y. Kodratoff, M. Sebag.
Preference Learning in Terminology Extraction: A ROC-based approach, in: Applied Stochastic Models and Data Analysis – ASMDA'05, 2005.
[14]
J. Azé, M. Roche, M. Sebag.
Bagging Evolutionary ROC-based Hypotheses Application to Terminology Extraction, in: ROCML 05, 2005.
[15]
N. Baskiotis, M. Sebag, O. Teytaud.
Inductive-deductive systems : a learning theory point of view, in: CAP'05, 2005, 2 p.
[16]
J. Bernauer, A. Poupon, J. Azé, J. Janin.
A docking analysis of the statistical physics of protein-protein recognition, in: JOBIM'05, 2005.
[17]
Y. Bonnemay, M. Sebag, O. Teytaud.
Convergence proofs, convergence rates and stopping criterions for multi-modal or multi-objective evolutionary algorithms, in: Evolution Artificielle, Lille, 2005, 12 p
http://hal.inria.fr/inria-00000545/en/.
[18]
N. Bredeche, L. Hugues.
Evolutionary Robotics : incremental learning of sequential behavior, in: Fourth IEEE International Conference on Development and Learning (ICDL 2005), 2005.
[19]
N. Bredeche, L. Hugues.
Speeding up Learning with Dynamic Environment Shaping in Evolutionary Robotics, in: Fifth international Workshop of Epigenetic Robotics (Epirob2005), 2005, p. 137-138.
[20]
A. Cornuéjols, C. Froidevaux, J. Mary.
Comparing and combining feature estimation methods for the analysis of microarray data, in: JOBIM-05 : Journées Ouvertes Biologie Informatique Mathématiques, Lyon, France, 2005.
[21]
A. Cornuéjols, M. Sebag.
Phase transition and inductive learning, in: Second Franco-Japanese Workshop on Information Search, Integration and Personalization (ISIP-05), Lyon, France, 2005.
[22]
A. Devert, N. Bredeche, M. Schoenauer.
Blindbuilder : A new encoding to evolve Lego-like structures, in: EuroGP'06, M. Ebner (editor), To appear, Springer Verlag, 2006.
[23]
C. Gagné, M. Schoenauer, M. Tomassini, M. Parizeau.
Genetic Programming, Validation Sets, and Parsimony Pressure, in: EuroGP'06, M. Ebner (editor), To appear, Springer Verlag, 2006.
[24]
S. Gelly, N. Bredeche, M. Sebag.
From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm., in: SARA, 2005, p. 107-120.
[25]
S. Gelly, N. Bredeche, M. Sebag.
HMM hiérarchiques et factorisés: mécanisme d'inférence et apprentissage à partir de peu de données., in: CAP, 2005, p. 143-144.
[26]
S. Gelly, N. Bredeche, M. Sebag.
Inference dans les HMM hierarchiques et factorises : changement de representation vers le formalisme des Reseaux Bayesien, in: EGC, Atelier sur les Modèles Graphiques Probabilistes, 2005, p. 35-44.
[27]
S. Gelly, J. Mary, O. Teytaud.
Taylor-based pseudo-metrics for random process fitting in dynamic programming., in: PDMIA, Lille, publié Non, 2005, p. 21-36
http://hal.inria.fr/inria-00000217/en/.
[28]
S. Gelly, O. Teytaud, N. Bredeche, M. Schoenauer.
A statistical learning theory approach of Bloat, in: GECCO - genetic and evolutionary computation conference, Washington, 2005, p. 1783-1784
http://hal.inria.fr/inria-00000546/en/.
[29]
S. Gelly, O. Teytaud.
Bayesian networks : a better than frequentist approach for parametrization, and a more accurate structural complexity measure than the number of parameters, in: CAP, Nice, 2005, p. 147-162
http://hal.inria.fr/inria-00000541/en/.
[30]
C. Germain, V. Breton, P. Clarysse, Y. Gaudeau, T. Glatard, E. Jeannot, Y. Legré, C. Loomis, J. Montagnat, J.-M. Moureaux, A. Osorio, X. Pennec, R. Texier.
Grid-enabling medical image analysis, in: 3rd Bio-Grid Workshop at IEEE CCGRID, 2005.
[31]
C. Germain-Renaud, D. Monnier-Ragaigne.
Grid Result Checking, in: Conf. Computing Frontiers, ACM, 2005, p. 87-96
http://doi.acm.org/10.1145/1062280.
[32]
C. Kavka, P. Roggero, M. Schoenauer.
Evolution of Voronoi based fuzzy recurrent controllers, in: GECCO'2005, H.-G. B. et al. (editor), ACM Press, ACM-SIGEVO, 2005, p. 1385-1392
http://hal.inria.fr/inria-00000539/en/.
[33]
S. Landau, O. Sigaud, M. Schoenauer.
ATNoSFERES revisited, in: Proc. GECCO-2005, Washington DC, H.-G. Beyer, et al. (editors), ACM Press, june 25-29 2005, p. 1867-1874
http://hal.inria.fr/inria-00000158/en/.
[34]
N. Pernot, A. Cornuéjols, M. Sebag.
Phase transition in grammatical inference, in: Int. Joint Conf. on Artificial Intelligence (IJCAI-05), Edinburgh, UK, 2005, p. 811-816.
[35]
N. Pernot, A. Cornuéjols, M. Sebag.
Phase transition in grammatical inference, in: CAP-05, Nice, 2005, p. 49-60.
[36]
M. Schoenauer, P. Savéant, V. Vidal.
Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal Planning, in: EvoCOP'06, J. Gottlieb, G. Raidl (editors), To appear, Springer Verlag, 2006.
[37]
M. Sebag, N. Tarrisson, O. Teytaud, J. Lefevre, S. Baillet.
A multi-objective multi-modal optimisation approach for mining stable spatio-temporal patterns, in: IJCAI'05, 2005, 6p p.
[38]
Y. Semet, M. Schoenauer.
An efficient memetic, permutation-based evolutionary algorithm for real-world train timetabling, in: CEC'05, B. McKay, et al. (editors), IEEE Press, 2005, p. 661-667
http://hal.inria.fr/inria-00000538/en/.
[39]
V. P. Singh, B. Duquet, M. Léger, M. Schoenauer.
Velocity determination in foothills using evolutionary algorithms, in: Thrust Belts and Foreland Basins, IFP, 2005.
[40]
V. P. Singh, M. Leger, M. Schoenauer.
Foothill Simulation using multi-objective evolutionary Algorithms, in: AAPG'05, American Association of Petroleum Geologists, 2005.
[41]
V. Singh, M. Schoenauer, M. Léger.
A geologically-sound representation for evolutionary multi-objective subsurface identification, in: CEC'05, B. McKay, et al. (editors), IEEE Press, 2005, p. 454-462
http://hal.inria.fr/inria-00000855/en/.
[42]
A. Termier, M.-C. Rousset, M. Sebag, K. Ohara, T. Washio, H. Motoda.
Computation-time efficient and robust attribute tree mining with DryadeParent, in: ECML/PKDD'05, 2005.
[43]
A. Termier, M.-C. Rousset, M. Sebag, K. Ohara, T. Washio, H. Motoda.
Efficient Mining of High Branching Factor Attribute Trees, in: ICDM 2005, IEEE Press, 2005.
[44]
O. Teytaud, M. Jebalia, A. Auger.
XSE: Quasi-random mutations for evolution strategies, in: Evolution Artificielle, Lille, 2005, p. 12-21
http://hal.inria.fr/inria-00000544/en/.

Miscellaneous

[45]
M. Amil.
Théorie VC pour la sélection de variables. Application au trafic routier, Technical report, Université Paris-Sud, 2005.
[46]
A. Amrani, J. Azé, Y. Kodratoff.
Logiciel d'aide à l'étiquetage morpho-syntaxique de textes de spécialité, 2005, Session Démonstration de logiciels, EGC 2005.
[47]
S. Deberles.
Un Observatoire de la Grille : schémas de fautes dans EGEE, Technical report, Université Paris-Sud, 2005
http://www.lri.fr/~cecile/DEMAIN/DEA_Samuel_Deberles.pdf.
[48]
A. Devert.
Evolution Artificielle de structures, Technical report, Université Paris 6, 2005.
[49]
C. Germain-Renaud.
Contributions à la modélisation et à l'optimisation des systèmes de calcul à grande échelle, 2005
http://www.lri.fr/~cecile/RAPH/germainHdr.pdf, HDR report, Université Paris-Sud.
[50]
C. Hartland.
Robotique Evolutionaire et Anticipation pour le controle, Technical report, Université Paris-Sud, 2005.
[51]
A. Martines.
Obstacle Avoidance: A Contribution to Evolutionary Robotics, Technical report, Universidad de Buenos Aires and LRI, 2005.
[52]
R. Ros.
Transition de phase en apprentissage artificiel. Pistes pour sa mise en évidence en robotique, Technical report, Université de Paris Sud – Orsay, 2005.
[53]
M. Zaslavskiy.
Méthodes de définition de sensibilité des variables à partir des résultats d'algorithmes évolutionnaires, Technical report, École Polytechnique, 2005.

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