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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

  • 1J. Gmys.

    Heterogeneous cluster computing for many-task exact optimization - Application to permutation problems, Université de Mons (UMONS) ; Université de Lille, December 2017.

    https://hal.inria.fr/tel-01652000

Articles in International Peer-Reviewed Journals

  • 2E. Alekseeva, M. Mezmaz, D. Tuyttens, N. Melab.

    Parallel multi-core hyper-heuristic GRASP to solve permutation flow-shop problem: Hyper-heuristique GRASP parallèle multi-coeur pour la résolution du flow-shop de permutation, in: Concurrency and Computation: Practice and Experience, May 2017, vol. 29, no 9, 15 p. [ DOI : 10.1002/cpe.3835 ]

    https://hal.inria.fr/hal-01419060
  • 3V. N. Coelho, T. A. Oliveira, I. M. Coelho, B. N. Coelho, P. J. Fleming, F. G. Guimarães, H. Ramalhinho, M. J. Souza, E.-G. Talbi, T. Lust.

    Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign, in: Computers and Operations Research, February 2017, vol. 78, pp. 578-587. [ DOI : 10.1016/j.cor.2016.09.008 ]

    https://hal.inria.fr/hal-01654721
  • 4F. Daolio, A. Liefooghe, S. Verel, H. Aguirre, K. Tanaka.

    Problem Features vs. Algorithm Performance on Rugged Multi-objective Combinatorial Fitness Landscapes, in: Evolutionary Computation, 2017, vol. 25, no 4, pp. 555–585. [ DOI : 10.1162/EVCO_a_00193 ]

    https://hal.archives-ouvertes.fr/hal-01380612
  • 5J. Gmys, M. Mezmaz, N. Melab, D. Tuyttens.

    IVM-based parallel branch-and-bound using hierarchical work stealing on multi-GPU systems, in: Concurrency and Computation: Practice and Experience, May 2017, vol. 29, no 9. [ DOI : 10.1002/cpe.4019 ]

    https://hal.archives-ouvertes.fr/hal-01673376
  • 6N. Melab, J. Gmys, M. Mezmaz, D. Tuyttens.

    Multi-core versus Many-core Computing for Many-task Branch-and-Bound applied to Big Optimization Problems, in: Future Generation Computer Systems, January 2017, 20 p, In Press, corrected proof, forthcoming.

    https://hal.inria.fr/hal-01419079
  • 7N. Melab, M. Mezmaz.

    Multi and many-core computing for parallel metaheuristics, in: Concurrency and Computation: Practice and Experience, May 2017, vol. 29, no 9. [ DOI : 10.1002/cpe.4116 ]

    https://hal.inria.fr/hal-01648278
  • 8A. Nakib, S. Ouchra, S. Chvai, E.-G. Talbi, L. Souquet.

    Deterministic metaheuristic based on fractal decomposition for large-scale optimization, in: Applied Soft Computing, December 2017, vol. 61, pp. 468-485.

    https://hal.inria.fr/hal-01660190
  • 9E.-G. Talbi, E. Alekseeva, Y. Kochetov.

    A matheuristic for the discrete bi-level problem with multiple objectives at the lower level, in: International Transactions in Operational Research, 2017, vol. 24, no 5, pp. 959-981.

    https://hal.inria.fr/hal-01654723
  • 10E.-G. Talbi, R. Todosijievic.

    The robust uncapacitated multiple allocation p-hub median problem, in: Computers and Industrial Engineering, 2017, vol. 110, pp. 322-332.

    https://hal.inria.fr/hal-01654718
  • 11C. Tiago, J. Gmys, d. C. J. Francisco Heron, N. Melab, D. Tuyttens.

    GPU-accelerated backtracking using CUDA Dynamic Parallelism, in: Concurrency and Computation: Practice and Experience, 2017, forthcoming.

    https://hal.inria.fr/hal-01648125
  • 12M. Vandromme, J. Jacques, J. Taillard, H. Arnaud, L. Jourdan, C. Dhaenens.

    Extraction and optimization of classification rules for temporal sequences: Application to hospital data, in: Knowledge-Based Systems, May 2017, vol. 122, pp. 148-158. [ DOI : 10.1016/j.knosys.2017.02.001 ]

    https://hal.archives-ouvertes.fr/hal-01564520

International Conferences with Proceedings

  • 13A. Blot, A. Pernet, L. Jourdan, M.-É. Kessaci-Marmion, H. H. Hoos.

    Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation, in: EMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Münster, Germany, H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, C. Grimme (editors), Evolutionary Multi-Criterion Optimization (EMO 2017), Springer, March 2017, vol. 10173, pp. 61-73. [ DOI : 10.1007/978-3-319-54157-0_5 ]

    https://hal.archives-ouvertes.fr/hal-01559690
  • 14B. Camus, F. Dufossé, A.-C. Orgerie.

    A stochastic approach for optimizing green energy consumption in distributed clouds, in: SMARTGREENS 2017 - International Conference on Smart Cities and Green ICT Systems, Porto, Portugal, April 2017.

    https://hal.inria.fr/hal-01475431
  • 15O. Cuate, E.-G. Talbi, B. Derbel, A. Liefooghe, O. Schütze.

    An approach fort he local exploration of discrete many objective optimization, in: EMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Münster, Germany, LNCS, March 2017, vol. 10173, pp. 135-150. [ DOI : 10.1007/978-3-319-54157-0_10 ]

    https://hal.inria.fr/hal-01654731
  • 16N. Dahmani, S. Krichen, E.-G. Talbi.

    An exact Epsilon constraint method for solving the multi-objective 2D vector packing problem, in: ICMSAO‘2017 7th Int. Conf. on Modeling Simulation and Applied Optimization, Sharjah, United Arab Emirates, 2017.

    https://hal.inria.fr/hal-01654730
  • 17Z. Garroussi, R. Ellaia, E.-G. Talbi.

    Hybrid Evolutionary Algorithm for Residential Demand Side Management with a Photovoltaic Panel and a Battery, in: ICCAIRO’2017 Conf. on Control, Artificial Intelligence, Robotics & Optimization, prague, Czech Republic, May 2017.

    https://hal.inria.fr/hal-01654829
  • 18Z. Garroussi, E.-G. Talbi, R. Ellaia.

    Hybrid multi-objective evolutionary algorithms for the residential demand side management with thermal and electrical loads, in: MIC’2017 Metaheuristics International Conference, Barcelone, Spain, July 2017.

    https://hal.inria.fr/hal-01654818
  • 19M. Gonzalez-Rodriguez, J.-J. Lopez-Espin, E.-G. Talbi, J. Aparicio.

    A parameterized scheme of metaheuristics with exact methods for determining the principle of least action in Data Envelopment Analysis, in: CEC’2017 Congress of Evolutionary Algortihms, San Sebastien, Spain, June 2017.

    https://hal.inria.fr/hal-01654836
  • 20A. Liefooghe, B. Derbel, S. Verel, H. Aguirre, K. Tanaka.

    A fitness landscape analysis of Pareto local search on bi-objective permutation flowshop scheduling problems, in: 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Münster, Germany, H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, C. Grimme (editors), Evolutionary Multi-Criterion Optimization (EMO 2017), Springer, March 2017, vol. 10173, no 422-437. [ DOI : 10.1007/978-3-319-54157-0_29 ]

    https://hal.archives-ouvertes.fr/hal-01496357
  • 21A. Liefooghe, B. Derbel, S. Verel, H. Aguirre, K. Tanaka.

    Towards landscape-aware automatic algorithm configuration: preliminary experiments on neutral and rugged landscapes, in: European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2017), Amsterdam, Netherlands, B. Hu, M. López-Ibáñez (editors), Evolutionary Computation in Combinatorial Optimization, Springer, April 2017, vol. 10197, pp. 215-232.

    https://hal.archives-ouvertes.fr/hal-01496347
  • 22H. Monzón, H. Aguirre, S. Verel, A. Liefooghe, B. Derbel, K. Tanaka.

    Closed state model for understanding the dynamics of MOEAs, in: Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, Genetic and Evolutionary Computation Conference (GECCO 2017), ACM, July 2017.

    https://hal.archives-ouvertes.fr/hal-01496329
  • 23L. Mousin, M.-E. Kessaci, C. Dhaenens.

    A new constructive heuristic for the No-Wait Flowshop Scheduling Problem, in: 11th Learning and Intelligent OptimizatioN Conference, Nizhny Novgorod, Russia, June 2017.

    https://hal.archives-ouvertes.fr/hal-01579775
  • 24A. Nakib, M. Hilia, F. Héliodore, E.-G. Talbi.

    Design of metaheuristic based on machine learning: A unified approach, in: IPDPS’2017 Int. Parallel and Distibuted Processing Symposium Workshop, Orlando, United States, May 2017, pp. 510-528.

    https://hal.inria.fr/hal-01654860
  • 25M. Sagawa, H. Aguirre, F. Daolio, A. Liefooghe, B. Derbel, S. Verel, K. Tanaka.

    Learning variable importance to guide recombination on many-objective optimization, in: 5th International Conference on Smart Computing and Artificial Intelligence (SCAI 2017), Hamamatsu, Japan, July 2017.

    https://hal.archives-ouvertes.fr/hal-01581247
  • 26J. Shi, Q. Zhang, B. Derbel, A. Liefooghe.

    A parallel tabu search for the unconstrained binary quadratic programming problem, in: IEEE Congress on Evolutionary Computation (CEC 2017), Donostia - San Sebastián, Spain, June 2017, pp. 557-564.

    https://hal.archives-ouvertes.fr/hal-01581361
  • 28E.-G. Talbi, N. Dupin.

    Matheuristics for a VRPTW with competence constraints, in: MIC’2017 - Metaheuristics International Conference, Barcelone, Spain, July 2017.

    https://hal.inria.fr/hal-01654864
  • 29E.-G. Talbi, M. Hajjem, H. Bouziri, K. Mellouli.

    Intelligent indoor evacuation guidance based on ant colony algorithm, in: IEEE AICCSA’2017 Int. Conf. on Computer Systems and Applications, Hammamet, Tunisia, October 2017.

    https://hal.inria.fr/hal-01654880

Conferences without Proceedings

  • 30O. ABDELKAFI, L. Idoumghar, J. Lepagnot.

    Distance Cooperation between Hybrid Iterative Tabu Search, in: Artificial Evolution EA 2017, Paris, France, October 2017.

    https://hal.archives-ouvertes.fr/hal-01672040
  • 31A. Blot, M.-É. Kessaci-Marmion, L. Jourdan.

    AMH: a new Framework to Design Adaptive Metaheuristics, in: 12th Metaheuristics International Conference, Barcelona, Spain, July 2017.

    https://hal.archives-ouvertes.fr/hal-01559687
  • 32A. Blot, M.-É. Kessaci-Marmion, L. Jourdan.

    AMH: une plate-forme pour le design et le contrôle automatique de métaheuristiques multi-objectif, in: ROADEF2017: 18ème Conférence ROADEF de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Metz, France, February 2017.

    https://hal.archives-ouvertes.fr/hal-01560444
  • 33O. Cuate, B. Derbel, A. Liefooghe, E.-G. Talbi, O. Schütze.

    An approach for the local exploration of discrete many objective optimization problems, in: 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Münster, Germany, Lecture Notes in Computer Science, March 2017, vol. 10173, pp. 135-150. [ DOI : 10.1109/TSMCB.2008.926329 ]

    https://hal.archives-ouvertes.fr/hal-01581433
  • 34L. Mousin, M.-E. Kessaci, C. Dhaenens.

    De nouvelles meilleures solutions pour le problème d'ordonnancement No-Wait Flowshop, in: ROADEF2017: 18ème Conférence ROADEF de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Metz, France, February 2017.

    https://hal.archives-ouvertes.fr/hal-01579762

Scientific Books (or Scientific Book chapters)

Internal Reports

  • 38F. Dufossé, K. Kaya, I. Panagiotas, B. Uçar.

    Further notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices, Inria - Research Centre Grenoble – Rhône-Alpes, September 2017, no RR-9095.

    https://hal.inria.fr/hal-01586245
References in notes
  • 39C. A. Coello Coello, D. A. Van Veldhuizen, G. B. Lamont (editors)

    Evolutionary algorithms for solving multi-objective problems, Kluwer Academic Press, 2002.
  • 40M. Basseur.

    Design of cooperative algorithms for multi-objective optimization: Application to the Flow-shop scheduling problem, University of Sciences and Technology of Lille, France, June 2005.
  • 41C. Cotta, E.-G. Talbi, E. Alba.

    Parallel hybrid approaches, in: Parallel Metaheuristics, USA, J. Wiley and Sons, 2005, pp. 347–370.
  • 42K. Deb.

    Multi-objective optimization using evolutionary algorithms, John Wiley and sons, 2001.
  • 43D. E. Goldberg.

    Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Massachusett, USA, 1989.
  • 44A. J. Nebro, F. Luna, E.-G. Talbi, E. Alba.

    Parallel multi-objective optimization, in: Parallel Metaheuristics, USA, J. Wiley and Sons, 2005, pp. 371–394.
  • 45E.-G. Talbi.

    A Taxonomy of Hybrid Metaheuristics, in: Journal of Heuristics, 2002, vol. 8, no 5, pp. 541–564.