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

Doctoral Dissertations and Habilitation Theses

[1]
N. Bredèche.
Contributions to Evolutionary Design of Embodied Agents, HDR – Habilitation à Diriger des Recherches, Université Paris-Sud, December 2009, Ph. D. Thesis.
[2]
A. Devert.
Building processes optimization: Toward an artificial ontogeny based approach, Université Paris-Sud, June 2009, Ph. D. Thesis.
[3]
C. Hartland.
Robust Robotic behavior Optimization, Université Paris-Sud, Ecold doctorale d'informatique, November 2009, Ph. D. Thesis.
[4]
F. Jiang.
Optimisation de la topologie de grands réseaux de neurones, Université Paris-Sud, Ecold doctorale d'informatique, December 2009, Ph. D. Thesis.
[5]
A. Rimmel.
Bandit-based optimization on graphs with application to library performance tuning, Université Paris-Sud, Ecold doctorale d'informatique, December 2009, Ph. D. Thesis.
[6]
R. Ros.
Real-Parameter Black-Box Optimisation: Benchmarking and Designing Algorithms, Université Paris-Sud, Orsay, France, December 2009, Ph. D. Thesis.

Articles in International Peer-Reviewed Journal

[7]
A. Auger, J. Bader, D. Brockhoff, E. Zitzler.
Hypervolume-based Multiobjective Optimization: Theoretical Foundations and Practical Implications, in: Theoretical Computer Science, 2010, submitted.
[8]
A. Auger, O. Teytaud.
Continuous Lunches are free plus the design of optimal optimization algorithms., in: Algorithmica, 2009.
[9]
P. Caillou, S. Aknine, S. Pinson.
Searching Pareto-optimal solutions for the problem of forming and restructuring coalitions in multi-agents systems, in: Group Decision and Negotiation, 2010, vol. 19
http://hal.archives-ouvertes.fr/inria-00370430/en/.
[10]
B. Cessac, H. Paugam-Moisy, T. Viéville.
Overview of facts and issues about neural coding by spikes, in: Journal of Physiology Paris, 2009
http://hal.inria.fr/inria-00407915/en/, to appear.
[11]
A. Fialho, L. Da Costa, M. Schoenauer, M. Sebag.
Analyzing Bandit-based Adaptive Operator Selection Mechanisms, in: Annals of Mathematics and Artificial Intelligence – Special Issue on Learning and Intelligent Optimization, 2009, submitted.
[12]
H. Fournier, O. Teytaud.
Lower Bounds for Comparison Based Evolution Strategies using VC-dimension and Sign Patterns, in: Algorithmica, 2010, (accepted with minor revision).
[13]
C. Furtlehner, J.-M. Lasgouttes, A. Auger.
Learning Multiple Belief Propagation Fixed Points for Real Time Inference, in: J. Phys. A, 2010, vol. 389, no 1, p. 149–163
http://www-roc.inria.fr/who/Cyril.Furtlehner/physicaA.pdf.
[14]
C. Germain-Renaud, O. Rana.
The Convergence of Clouds, Grids, and Autonomics - GMAC Panel Session report, in: IEEE Internet Computing, 11 2009, vol. 13, 9 p
http://hal.inria.fr/inria-00434044/en/.
[15]
N. Hansen, A. Niederberger, L. Guzzella, P. Koumoutsakos.
A Method for Handling Uncertainty in Evolutionary Optimization with an Application to Feedback Control of Combustion, in: IEEE Transactions on Evolutionary Computation, 2009, vol. 13, no 1, p. 180–197
http://hal.inria.fr/inria-00276216/en/.
[16]
M. Jebalia, A. Auger, N. Hansen.
Log linear convergence and divergence of the scale-invariant (1+1)-ES in noisy environments, in: Algorithmica, 2010, conditionally accepted p
http://hal.inria.fr/inria-00433347/en/, conditionally accepted.
[17]
C.-S. Lee, M.-H. Wang, G. Chaslot, J.-B. Hoock, A. Rimmel, O. Teytaud, S.-R. Tsai, S.-C. Hsu, T.-P. Hong.
The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments, in: IEEE Transactions on Computational Intelligence and AI in games, 2009
http://hal.inria.fr/inria-00369786/en/.
[18]
M. Nicolau, M. Schoenauer.
On the Evolution of Scale-Free Topologies with a Gene Regulatory Network Model, in: BioSystems Journal, December 2009, vol. 98, no 3, p. 137-148
http://dx.doi.org/10.1016/j.biosystems.2009.06.006.
[19]
T. Suttorp, N. Hansen, C. Igel.
Efficient Covariance Matrix Update for Variable Metric Evolution Strategies, in: Machine Learning, 2009, vol. 72, no 2, p. 167–197.
[20]
D. Tregouet, I. König, J. Erdemann, A. Munteanu, P. Braund, A. Hall, A. Grohennig, P. Linsel-Nitschke, C. Perret, M. Desuremain, T. Meitinger, B. Wright, M. Preuss, A. Balmforth, S. Ball, C. Meisinger, C. Germain-Renaud, A. Evans, D. Arveiller, G. Luc, J.-B. Ruidavets, C. Morrison, P. Van Der Harst, S. Schreiber, K. Neureuther, A. Schäfer, P. Bugert, N. El Mokhtari, J. Schrezenmeir, K. Stark, D. Rubin, H.-E. Wichmann, C. Hengstenberg, W. Ouwehand, Trust Case Control Consortium, Wellcome, Consortium, Cardiogenics, A. Ziegler, L. Tiret, J. Thompson, F. Cambien, H. Schunkert, N. Samani.
Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease, in: Nature Genetics, 2009, vol. 41, p. 283-285
http://hal.inria.fr/inria-00433429/en/.

Invited Conferences

[21]
A. Auger, N. Hansen, J. Perez Zerpa, R. Ros, M. Schoenauer.
Experimental Comparisons of Derivative Free Optimization Algorithms, in: 8th International Symposium on Experimental Algorithms, Dortmund, J. Vahrenhold (editor), LNCS, Springer Verlag, 2009, no 5526, p. 3-15
http://hal.inria.fr/inria-00397334/en/.

International Peer-Reviewed Conference/Proceedings

[22]
M. Amil, N. Bredèche, C. Gagné, S. Gelly, M. Schoenauer, O. Teytaud.
A Statistical Learning Perspective of Genetic Programming, in: Proceedings of EuroGP 09, Springer Veralg, 2009
http://hal.inria.fr/inria-00369782/en/.
[23]
A. Arbelaez, Y. Hamadi.
Exploiting Weak Dependencies in Tree-based Search, in: Proceedings of the 24th Annual ACM Symposium on Applied Computing, ACM Press, ACM, 2009, p. 1385-1391
http://hal.inria.fr/inria-00344179/fr/, to appear.
[24]
P. Audouard, G. Chaslot, J.-B. Hoock, A. Rimmel, J. Perez, O. Teytaud.
Grid coevolution for adaptive simulations; application to the building of opening books in the game of Go, in: EvoGames, Tuebingen Allemagne, Springer Verlag, 2009
http://hal.inria.fr/inria-00369783/en/.
[25]
A. Auger.
Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2447-2452.
[26]
A. Auger.
Benchmarking the (1+1)-ES with One-Fifth Success Rule on the BBOB-2009 noisy Function Testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2453-2458.
[27]
A. Auger, J. Bader, D. Brockhoff, E. Zitzler.
Articulating User Preferences in Many-Objective Problems by Sampling the Weighted Hypervolume, in: Genetic and Evolutionary Computation Conference (GECCO 2009), G. Raidl, etal (editors), ACM, July 2009, p. 555-562.
[28]
A. Auger, J. Bader, D. Brockhoff, E. Zitzler.
Investigating and Exploiting the Bias of the Weighted Hypervolume to Articulate User Preferences, in: Genetic and Evolutionary Computation Conference (GECCO 2009), G. Raidl, etal (editors), ACM, July 2009, p. 563-570.
[29]
A. Auger, J. Bader, D. Brockhoff, E. Zitzler.
Theory of the Hypervolume Indicator: Optimal $ \mu$ -Distributions and the Choice of the Reference Point, in: Foundations of Genetic Algorithms (FOGA 2009), ACM, 2009, p. 87-102.
[30]
A. Auger, N. Hansen.
Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2459-2466.
[31]
A. Auger, N. Hansen.
Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2467-2472.
[32]
A. Auger, N. Hansen, J. Perez Zerpa, R. Ros, M. Schoenauer.
Empirical comparisons of several derivative free optimization algorithms, in: Acte du 9ème colloque national en calcul des structures, May 2009.
[33]
A. Auger, R. Ros.
Benchmarking the pure random search on the BBOB-2009 noisy testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2485-2490.
[34]
A. Auger, R. Ros.
Benchmarking the pure random search on the BBOB-2009 testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2479-2484.
[35]
G. Baele, N. Bredèche, E. Haasdijk, S. Maere, N. Michiels, Y. V. de Peer, T. Schmickl, C. Schwarzer, R. Thenius.
Open-ended On-board Evolutionary Robotics for Robot Swarms, in: Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC 2009), 2009.
[36]
V. Berthier, H. Doghmen, O. Teytaud.
Consistency Modifi`cations for Automatically Tuned Monte-Carlo Tree Search, in: Lion4, 2010
http://hal.inria.fr/inria-00437146/en/, To appear.
[37]
J. Bibai, M. Schoenauer, P. Savéant.
Divide-And-Evolve Facing State-of-the-art Temporal Planners during the 6th International Planning Competition, in: 9th European Conf. on Evolutionary Computation in Combinatorial Optimization (EvoCOP'09), Tübingen Allemagne, C. Cotta, P. Cowling (editors), LNCS, Springer-Verlag, 2009, no 5482, p. 133-144
http://hal.inria.fr/inria-00356069/fr/.
[38]
J. Bibai, P. Savéant, M. Schoenauer, V. Vidal.
Learning Divide-and-Evolve Parameter Configurations with Racing, in: ICAPS 2009 proceedings of the Workshop on Planning and Learning, Thessaloniki Grèce, A. Coles, A. Coles, S. J. Celorrio, S. F. Arregui, T. de la Rosa (editors), AAAI press, International Conference on Planning and Scheduling and University of Macedonia, 2009
http://hal.inria.fr/inria-00406626/en/.
[39]
J. Bibai, P. Savéant, M. Schoenauer, V. Vidal.
On the Benefit of Sub-Optimality within the Divide-and-Evolve Scheme, in: EvoCOP 2010, P. Merz, P. Cowling (editors), Springer-Verlag, 2010
http://hal.inria.fr/inria-00443984/fr/.
[40]
N. Bredèche, E. Haasdijk, A. Eiben.
On-line, On-board Evolution of Robot Controllers, in: Proceedings of the 9th international conference on Artificial Evolution (Evolution Artificielle - EA'09), 2009.
[41]
R. Busa-Fekete, B. Kégl.
Accelerating AdaBoost using UCB, in: KDDCup 2010 (JMLR workshop and conference proceedings), Paris, France, 2009, vol. 7, p. 111-122.
[42]
P. Caillou, C. Curchod, T. Baptista.
Simulation of the Rungis Wholesale Market: Lessons on the Calibration, Validation and Usage of a Cognitive Agent-Based Simulation, in: IAT 2009, Milano Italie, IEEE, 09 2009, vol. 2, p. 70-73
http://hal.archives-ouvertes.fr/inria-00429616/en/.
[43]
P. Caillou, M. Sebag.
Pride and Prejudice on a Centralized Academic Labor Market, in: Artificial Economics 09 LNEMS, Valladolid Espagne, LNEMS, Springer, 2009
http://hal.inria.fr/inria-00380541/en/.
[44]
G. Chaslot, C. Fiter, J.-B. Hoock, A. Rimmel, O. Teytaud.
Adding expert knowledge and exploration in Monte-Carlo Tree Search, in: Advances in Computer Games, Pamplona Espagne, Springer Verlag, 2009
http://hal.inria.fr/inria-00386477/en/.
[45]
G. Chaslot, J.-B. Hoock, F. Teytaud, O. Teytaud.
On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers, in: ESANN, Bruges Belgique, 2009
http://hal.inria.fr/inria-00380125/en/.
[46]
L. Da Costa, M. Schoenauer.
Bringing Evolutionary Computation to Industrial Applications with GUIDE, in: Genetic and Evolutionary Computation Conference (GECCO), G. Raidl, etal (editors), ACM Press, ACM, 2009
http://hal.inria.fr/inria-00375419/en/.
[47]
F. De Mesmay, A. Rimmel, Y. Voronenko, M. Püschel.
Bandit-Based Optimization on Graphs with Application to Library Performance Tuning, in: ICML, Montréal Canada, 2009
http://hal.inria.fr/inria-00379523/en/.
[48]
F.-M. De Rainville, C. Gagné, O. Teytaud, D. Laurendeau.
Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm, in: Genetic and Evolutionary Computation Conference (GECCO 2009), Montréal Canada, G. Raidl, etal (editors), ACM Press, 2009
http://hal.inria.fr/inria-00386475/en/.
[49]
R. Dilão, D. Muraro, M. Nicolau, M. Schoenauer.
Validation of a morphogenesis model of Drosophila early development by a multi-objective evolutionary optimization algorithm, in: European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, C. Pizzuti, M. D. Ritchie, M. Giacobini (editors), LNCS, Springer Verlag, April 2009, no 5483, p. 176-190, Best Paper Award.
[50]
S. Doncieux, J.-B. Mouret, N. Bredèche.
Exploring New Horizons in Evolutionary Design of Robots, in: CD Proceedings of IROS 2009 Workshop on Exploring new horizons in Evolutionary Design of Robots (Evoderob09), 2009, p. 5-12.
[51]
A. Fialho, L. D. Costa, M. Schoenauer, M. Sebag.
Dynamic Multi-Armed Bandits and Extreme Value-based Rewards for Adaptive Operator Selection in Evolutionary Algorithms, in: LION'09: Proceedings of the 3rd International Conference on Learning and Intelligent OptimizatioN, Springer Verlag, January 2009.
[52]
A. Fialho, M. Schoenauer, M. Sebag.
Analysis of Adaptive Operator Selection Techniques on the Royal Road and Long K-Path Problems, in: Genetic and Evolutionary Computation Conference (GECCO 2009), G. Raidl, etal (editors), ACM Press, July 2009, p. 779–786.
[53]
N. Hansen.
Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2389-2395.
[54]
N. Hansen.
Benchmarking a BI-Population CMA-ES on the BBOB-2009 Noisy Testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2397-2402.
[55]
N. Hansen.
Benchmarking the Nelder-Mead Downhill Simplex Algorithm With Many Local Restarts, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2403-2408.
[56]
C. Hartland, N. Bredèche, M. Sebag.
Memory-Enhanced Evolutionary Robotics: The Echo State Network Approach, in: Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC 2009), 2009.
[57]
B. Kégl, R. Busa-Fekete.
Boosting products of base classifiers, in: International Conference on Machine Learning, Montreal, Canada, 2009, vol. 26, p. 497-504.
[58]
S. Kernbach, H. Hamann, J. Stradner, R. Thenius, T. Schmickl, A. van Rossum, M. Sebag, N. Bredèche, Y. Yao, G. Baele, Y. V. de Peer, J. Timmis, M. Mohktar, A. Tyrrell, A. Eiben, S. McKibbin, W. Liu, A. F. Winfield.
On adaptive self-organization in artificial robot organisms, in: Proceedings of the First IEEE International Conference on Adaptive and Self-adaptive Systems and Applications (IEEE ADAPTIVE 2009), 2009.
[59]
C.-S. Lee, M.-H. Wang, T.-P. Hong, G. Chaslot, J.-B. Hoock, A. Rimmel, O. Teytaud, Y.-H. Kuo.
A Novel Ontology for Computer Go Knowledge Management, in: IEEE FUZZ, Jeju Corée, République de, 2009
http://hal.inria.fr/inria-00386476/en/.
[60]
R. Martinez, H. Paugam-Moisy.
Algorithms for structural and dynamical polychronous groups detection, in: ICANN'2009, International Conference on Artificial Neural Networks Artificial Neural Networks, Limassol Chypre, LNCS, Lecture Notes in Computer Science, Springer, IEEE - INNS, 2009, vol. 5769, p. 75-84
http://hal.inria.fr/inria-00425514/en/.
[61]
J. Maturana, A. Fialho, F. Saubion, M. Schoenauer, M. Sebag.
Extreme Compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection, in: CEC'09: Proceedings of the IEEE International Conference on Evolutionary Computation, IEEE, May 2009, p. 365–372.
[62]
J.-M. Montanier, N. Bredèche.
Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm, in: CD Proceedings of IROS 2009 Workshop on Exploring new horizons in Evolutionary Design of Robots (Evoderob09), 2009, p. 37-43.
[63]
J. Monteiro, P. Caillou, M. Netto.
An Agent Model Using Polychronous Networks, in: Colibri, Bento Gonçalves Brésil, 2009
http://hal.inria.fr/inria-00393065/en/.
[64]
M. Nicolau.
Application of a simple binary genetic algorithm to a noiseless testbed benchmark, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009
http://hal.inria.fr/inria-00377093/en/.
[65]
M. Nicolau, M. Schoenauer, W. Banzhaf.
Evolving Genes to Balance a Pole, in: 13th European Conference on Genetic Programming (EuroGP 2010), A. I. Esparcia, A. Eckart (editors), Springer, 2010.
[66]
M. Nicolau, M. Schoenauer.
Evolving Specific Network Statistical Properties using a Gene Regulatory Network Model, in: Genetic and Evolutionary Computation Conference (GECCO 2009), G. Raidl, etal (editors), ACM, July 2009, p. 723-730.
[67]
J. Perez, C. Germain-Renaud, B. Kégl, C. Loomis.
Responsive Elastic Computing, in: Workshop Grids Meet Autonomic Computing, associated with International Conference on Autonomic Computing, Barcelone, June 15th-19th, ACM, 2009, p. 55-64
http://hal.inria.fr/inria-00384970/en/.
[68]
J. Perez, C. Germain-Renaud, B. Kégl, C. Loomis.
Toward Responsive Grids through Multi Objective Reinforcement Learning, in: 4th EGEE User Forum, 2009.
[69]
P. Rolet, M. Sebag, O. Teytaud.
Boosting Active Learning to Optimality: a Tractable Monte-Carlo, Billiard-based Algorithm, in: ECML, Bled Slovénie, 2009, p. 302-317
http://hal.inria.fr/inria-00433866/en/.
[70]
P. Rolet, M. Sebag, O. Teytaud.
Optimal robust expensive optimization is tractable, in: Genetic and Evolutionary Computation Conference (GECCO 2009), Montréal Canada, G. Raidl, etal (editors), ACM Press, ACM, 2009, p. 1951-1956
http://hal.inria.fr/inria-00374910/en/, G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.6: Optimization, I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.8: Problem Solving, Control Methods, and Search.
[71]
P. Rolet, O. Teytaud.
Bandit-based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis, in: Lion4, Venice Italie, 2010
http://hal.inria.fr/inria-00437140/en/, To appear.
[72]
R. Ros.
Benchmarking sep-CMA-ES on the BBOB-2009 function testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2435–2440.
[73]
R. Ros.
Benchmarking sep-CMA-ES on the BBOB-2009 noisy testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2441–2446.
[74]
R. Ros.
Benchmarking the BFGS algorithm on the BBOB-2009 function testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2409–2414.
[75]
R. Ros.
Benchmarking the BFGS algorithm on the BBOB-2009 noisy testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2415–2420.
[76]
R. Ros.
Benchmarking the NEWUOA on the BBOB-2009 function testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2421–2428.
[77]
R. Ros.
Benchmarking the NEWUOA on the BBOB-2009 noisy testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, G. Raidl, etal (editors), ACM, July 2009, p. 2429–2434.
[78]
F. Teytaud, O. Teytaud.
Creating an Upper-Confidence-Tree program for Havannah, in: ACG 12, Pamplona Espagne, 2009
http://hal.inria.fr/inria-00380539/en/.
[79]
F. Teytaud, O. Teytaud.
On the parallel speed-up of Estimation of Multivariate Normal Algorithm and Evolution Strategies, in: EvoNum, Proceedings of EvoStar workshop 2009, Tuebingen Allemagne, Springer Verlag, 2009, vol. EvoNum
http://hal.inria.fr/inria-00369781/en/.
[80]
F. Teytaud, O. Teytaud.
Why one must use reweighting in Estimation Of Distribution Algorithms, in: Genetic and Evolutionary Computation Conference (GECCO 2009), G. Raidl, etal (editors), ACM Press, 2009
http://hal.inria.fr/inria-00369780/en/.
[81]
T. Voß, N. Hansen, C. Igel.
Recombination for Learning Strategy Parameters in the MO-CMA-ES, in: Fifth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2009), Springer-Verlag, 2009, p. 155-168.
[82]
X. Zhang, C. Furtlehner, J. Perez, C. Germain-Renaud, M. Sebag.
Toward Autonomic Grids: Analyzing the Job Flow with Affinity Streaming, in: 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Paris France, 2009, p. 987-995
http://hal.inria.fr/inria-00393825/en/.
[83]
X. Zhang, C. Furtlehner, M. Sebag, C. Germain-Renaud.
Grid Monitoring by Online Clustering, in: 4th EGEE User Forum, Catania, Sicily, Italy, 2009.
[84]
X. Zhang, M. Sebag, C. Germain-Renaud.
Multi-scale Real-time Grid Monitoring with Job Stream Mining, in: IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2009), Shanghai China, 2009, p. 420-427
http://hal.inria.fr/inria-00367544/en/, EGEE-III PASCAL.

National Peer-Reviewed Conference/Proceedings

[85]
L. Arnold, H. Paugam-Moisy, M. Sebag.
Optimisation de la Topologie pour les Réseaux de Neurones Profonds, in: Proc. RFIA, 2010, to appear.
[86]
J. Bibai, M. Schoenauer, P. Savéant.
Construction des sèries d'états dans l'algorithme Divide-and-Evolve, in: ROADEF09, Livre des résumés, Nancy France, ROADEF'09, 2009, p. 98-99
http://hal.inria.fr/inria-00354272/fr/.
[87]
P. Delarboulas, M. Sebag.
Critères non supervisés pour contrôleurs robotiques embarqués, in: Proc. RFIA, 2010, to appear.
[88]
C. Hartland, N. Bredèche, M. Sebag.
Robotique Evolutionnaire et Mémoire, in: Proceedings of the Conférence francophone sur l'apprentissage automatique (CAp09), 2009, p. 161–172.
[89]
P. Rolet, M. Sebag, O. Teytaud.
Upper Confidence Trees and Billiards for Optimal Active Learning, in: CAP09, Hammamet, Tunisie, 2009
http://hal.inria.fr/inria-00369787/en/.

Workshops without Proceedings

[90]
A. Arbelaez, Y. Hamadi, M. Sebag.
Online Heuristic Selection in Constraint Programing, in: International Symposium on Combinatorial Search, 2009.
[91]
C. Curchod, P. Caillou, T. Baptista.
Which Buyer-Supplier Strategies on Uncertain Markets? A Multi-Agents Simulation, in: Strategic Managemtn Society, Washington États-Unis d'Amérique, 2009
http://hal.inria.fr/inria-00380540/en/.
[92]
R. Gaudel, M. Sebag.
Feature Selection as a one-player game, in: 2nd NIPS Workshop on Optimization for Machine Learning, 2009.

Scientific Books (or Scientific Book chapters)

[93]
D. Brockhoff.
Many-Objective Optimization and Hypervolume Based Search, Shaker Verlag, Aachen, Germany, 2009.
[94]
D. Brockhoff.
Theoretical Aspects of Evolutionary Multiobjective Optimization, in: Theory of Randomized Search Heuristics: Foundations and Recent Developments, A. Auger, B. Doerr (editors), World Scientific Publishing, 2010, accepted for publication.
[95]
Y. Colette, N. Hansen, G. Pujol.
Vers une Programmation Orientée Objet des Optimiseurs, in: Opimisation multidisciplinaire en mécanique 2. Réduction de modèles, robustesse, fiabilité, réalisations logicielles, Paris, London, Chippenham, Méthodes Numériques en Mécanique, Hermes Science, Lavoisier, April 2009, vol. 2, chap. 7
http://hal.archives-ouvertes.fr/hal-00409906/en/.
[96]
C. Germain-Renaud, V. Breton, P. Clarysse, B. Delhay, Y. Gaudeau, T. Glatard, E. Jeannot, Y. Legré, J. Montagnat, J.-M. Moureaux, A. Osorio, X. Pennec, J. Schaerer, R. Texier.
Grid analysis of radiological data, in: Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, M. Cannataro (editor), Medical Information Science Reference, 05 2009, chap. 19, . p
http://hal.archives-ouvertes.fr/hal-00382594/en/.
[97]
N. Hansen, A. Auger.
Principled Design of Continuous Stochastic Search in Practice: the CMA Evolution Strategy, in: Theory and Principled Methods for the Design of Metaheuristics, Y. Borenstein, A. Moraglio (editors), Springer, 2010, submitted p, submitted.
[98]
H. Paugam-Moisy, S. M. Bothe.
Computing with Spiking Neuron Networks, in: The Handbook of Natural Computing, Springer, 2009, to appear.

Books or Proceedings Editing

[99]
A. Auger, B. Doerr (editors)
Theory of Randomized Seach Heuristics–Foundations and Recent Developments, Theoretical Computer Science, World Scientific, 2010, vol. 1.

Internal Reports

[100]
D. Brockhoff.
Theoretical Aspects of Evolutionary Multiobjective Optimization—A Review, INRIA Saclay—Île-de-France, September 2009, no RR-7030, Rapport de Recherche.
[101]
T. Elteto, C. Germain-Renaud, P. Bondon.
Discovering Linear Models of Grid Workload, INRIA, 2009, no RR-7112
http://hal.inria.fr/inria-00435561/en/, Research Report.
[102]
S. Finck, N. Hansen, R. Ros, A. Auger.
Real-Parameter Black-Box Optimization Benchmarking 2009: Presentation of the Noiseless Functions, Research Center PPE, 2009, no 2009/20, Technical report.
[103]
S. Finck, N. Hansen, R. Ros, A. Auger.
Real-Parameter Black-Box Optimization Benchmarking 2009: Presentation of the Noisy Functions, Research Center PPE, 2009, no 2009/21, Technical report.
[104]
S. Finck, R. Ros.
Real-Parameter Black-Box Optimization Benchmarking 2009 Software: User Documentation, INRIA, 2009, no RT-0372
http://hal.inria.fr/inria-00433590, Technical report.
[105]
C. Furtlehner, M. Sebag, X. Zhang.
Scaling Analysis of the Affinity Propagation Algorithm, INRIA, 2009, no RR-7046
http://hal.inria.fr/inria-00420407/en/, Research Report.
[106]
N. Hansen, A. Auger, S. Finck, R. Ros.
Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup, INRIA, 2009, no RR-6828
http://hal.inria.fr/inria-00362649/en/, Technical report.
[107]
N. Hansen, A. Auger, S. Finck, R. Ros.
Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup, INRIA, 2009, no RR-6828
http://hal.inria.fr/inria-00362649/en/, Research Report.
[108]
N. Hansen, S. Finck, R. Ros, A. Auger.
Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions, INRIA, 2009, no RR-6829
http://hal.inria.fr/inria-00362633/en/, Technical report.
[109]
N. Hansen, S. Finck, R. Ros, A. Auger.
Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions, INRIA, 2009, no RR-6829
http://hal.inria.fr/inria-00362633/en/, Research Report.
[110]
N. Hansen, S. Finck, R. Ros, A. Auger.
Real-Parameter Black-Box Optimization Benchmarking 2009: Noisy Functions Definitions, INRIA, 2009, no RR-6869
http://hal.inria.fr/inria-00369466/en, Technical report.

Scientific Popularization

[111]
N. Bredèche.
Évolution de robots autonomes et autres créatures artificielles, in: Les mondes darwiniens, L'évolution de l'évolution, T. Heams, P. Huneman, G. Lecointre, M. Silberstein (editors), Editions Syllepse, 2009, chap. 31, p. 747-768.
[112]
M. Schoenauer.
Les algorithmes évolutionnaires, in: Les mondes darwiniens, L'évolution de l'évolution, T. Heams, P. Huneman, G. Lecointre, M. Silberstein (editors), Editions Syllepse, 2009, chap. 31, p. 731-746.

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