U. Aickelin, E. Burke, L. , and J. , An Evolutionary Squeaky Wheel Optimization Approach to Personnel Scheduling, IEEE Transactions on Evolutionary Computation, vol.13, issue.2, pp.433-443, 2009.
DOI : 10.1109/TEVC.2008.2004262

U. Aickelin and K. A. Dowsland, Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem, Journal of Scheduling, vol.5, issue.3, pp.139-153, 2000.
DOI : 10.1002/(SICI)1099-1425(200005/06)3:3<139::AID-JOS41>3.0.CO;2-2

E. Alba, Parallel Metaheuristics: A New Class of Algorithms, 2005.
DOI : 10.1002/0471739383

A. Rodriguez and S. , From Analysis to Design of Holonic Multi-Agent Systems: A Framework, Methodological Guidelines and Applications, 2005.

M. Bader-el-den and R. Poli, Generating SAT Local-Search Heuristics Using a GP Hyper-Heuristic Framework, Artificial Evolution, number 4926 in Lecture Notes in Computer Science, pp.37-49, 2008.
DOI : 10.1007/978-3-540-79305-2_4

R. Bai, An Investigation of Novel Approaches for Optimizing Retail Shelf Space Allocation, 2005.

R. Bai, E. K. Burke, M. Gendreau, G. Kendall, and B. Mccollum, Memory length in hyperheuristics: An empirical study, IEEE Symposium on Computational Intelligence in Scheduling SCIS '07, pp.173-178, 2007.

R. Bai and G. Kendall, An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-Heuristic, Metaheuristics: Progress as Real Problem Solvers, number 32 in Operations Research/Computer Science Interfaces Series, pp.87-108, 2005.
DOI : 10.1007/0-387-25383-1_4

J. Blais and J. Rousseau, Overview of HASTUS Current and Future Versions, Computer-Aided Transit Scheduling, number 308 in Lecture Note in Economics Mathematical Systems, pp.175-187, 1988.
DOI : 10.1007/978-3-642-85966-3_15

C. Blum and A. Roli, Metaheuristics in combinatorial optimization, ACM Computing Surveys, vol.35, issue.3, pp.268-308, 2003.
DOI : 10.1145/937503.937505

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

E. Burke, E. Hart, G. Kendall, J. Newall, E. Hart et al., Handbook of metaheuristics , Kluwer Academic, chap Hyper-Heuristics: An Emerging Direction in Modern Search Technology, 2003.

E. Burke, G. Kendall, L. Silva, D. O-'brien, R. Soubeiga et al., An Ant Algorithm Hyperheuristic for the Project Presentation Scheduling Problem, 2005 IEEE Congress on Evolutionary Computation, pp.2263-2270, 2005.
DOI : 10.1109/CEC.2005.1554976

E. Burke, G. Kendall, and E. Soubeiga, A Tabu-Search Hyperheuristic for Timetabling and Rostering, Journal of Heuristics, vol.9, issue.6, pp.451-470, 2003.
DOI : 10.1023/B:HEUR.0000012446.94732.b6

E. Burke, B. Mccollum, A. Meisels, S. Petrovic, and R. Qu, A graph-based hyper-heuristic for educational timetabling problems, European Journal of Operational Research, vol.176, issue.1, pp.177-192, 2007.
DOI : 10.1016/j.ejor.2005.08.012

E. K. Burke, T. Curtois, M. R. Hyde, G. Kendall, G. Ochoa et al., Hyflex: A flexible framework for the design and analysis of hyper-heuristics, Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA 09), pp.790-797, 2009.

E. K. Burke, M. Gendreau, M. Hyde, G. Kendall, G. Ochoa et al., Hyperheuristics: a survey of the state of the art, Journal of the Operational Research Society, 2013.

E. K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Özcan et al., A Classification of Hyper-heuristic Approaches, Handbook of Metaheuristics, number 146 in International Series in Operations Research & Management Science, pp.449-468, 2010.
DOI : 10.1007/978-1-4419-1665-5_15

E. K. Burke, M. R. Hyde, K. , G. T. Beyer, H. Burke et al., Evolving Bin Packing Heuristics with Genetic Programming, Parallel Problem Solving from Nature -PPSN IX, number 4193 in Lecture Notes in Computer Science, pp.860-869, 2006.
DOI : 10.1007/11844297_87

E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan et al., Exploring Hyper-heuristic Methodologies with Genetic Programming, Computational Intelligence, number 1 in Intelligent Systems Reference Library, pp.177-201, 2009.
DOI : 10.1007/978-3-642-01799-5_6

E. K. Burke, G. Kendall, M. M?s?r, and E. Özcan, Monte Carlo hyper-heuristics for examination timetabling, Annals of Operations Research, vol.13, issue.2, pp.73-90, 2012.
DOI : 10.1007/s10479-010-0782-2

L. Busoniu, R. Babuska, D. Schutter, and B. , A Comprehensive Survey of Multiagent Reinforcement Learning, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.38, issue.2, pp.156-172, 2008.
DOI : 10.1109/TSMCC.2007.913919

P. Calégari, G. Coray, A. Hertz, D. Kobler, and P. Kuonen, A taxonomy of evolutionary algorithms in combinatorial optimization, Journal of Heuristics, vol.5, issue.2, pp.145-158, 1999.
DOI : 10.1023/A:1009625526657

L. Cavique, C. Rego, and I. Themido, Subgraph ejection chains and tabu search for the crew scheduling problem, The Journal of the Operational Research Society, issue.6, p.50608, 1999.

B. Chaib-draa, B. Moulin, R. Mandiau, and P. Millot, Trends in distributed artificial intelligence, Artificial Intelligence Review, vol.59, issue.1, pp.35-66, 1992.
DOI : 10.1007/BF00155579

M. Chen and H. Niu, Research on the Scheduling Problem of Urban Bus Crew Based on Impartiality, Procedia - Social and Behavioral Sciences, vol.43, pp.503-511, 2012.
DOI : 10.1016/j.sbspro.2012.04.123

P. Chen, G. Kendall, and G. Berghe, An Ant Based Hyper-heuristic for the Travelling Tournament Problem, 2007 IEEE Symposium on Computational Intelligence in Scheduling, pp.19-26, 2007.
DOI : 10.1109/SCIS.2007.367665

K. Chew, J. Pang, Q. Liu, J. Ou, and C. Teo, An optimization based approach to the train operator scheduling problem at singapore MRT, Annals of Operations Research, vol.108, pp.1-4111, 2001.

C. Cotta, M. Sevaux, and K. Sörensen, Adaptive and multilevel metaheuristics, 2008.
DOI : 10.1007/978-3-540-79438-7

URL : https://hal.archives-ouvertes.fr/hal-00369067

P. Cowling, G. Kendall, and E. Soubeiga, A Hyperheuristic Approach to Scheduling a Sales Summit, In Lecture Notes In Computer Science, vol.2079, pp.176-190, 2001.
DOI : 10.1007/3-540-44629-X_11

P. Cowling, G. Kendall, and E. Soubeiga, A parameter-free hyperheuristic for scheduling a sales summit, Proceedings of the 4th Metaheuristic International Conference, pp.127-131, 2001.

P. Cowling and E. Soubeiga, Neighborhood structures for personnel scheduling: A summit meeting scheduling problem, Proceedings of the 3rd International Conference on the Practice and Theory of Automated Timetabling. American Association for Artificial Intelligence, 2000.

T. G. Crainic and M. Toulouse, Parallel Strategies for Meta-Heuristics, Handbook of Metaheuristics, number 57 in International Series in Operations Research & Management Science, pp.475-513, 2003.
DOI : 10.1007/0-306-48056-5_17

J. Créput and A. Koukam, The memetic self-organizing map approach to the vehicle routing problem, Soft Computing, vol.15, issue.6, pp.1125-1141, 2008.
DOI : 10.1007/s00500-008-0281-4

J. R. Daduna, I. Branco, and J. M. Paixão, Computer-aided transit scheduling: proceedings of the Sixth International Workshop on Computer-aided Scheduling of Public Transport, 1995.

J. R. Daduna and M. Mojsilovic, Computer-Aided Vehicle and Duty Scheduling Using the HOT Programme System, Computer-Aided Transit Scheduling, number 308 in Lecture Note in Economics Mathematical Systems, pp.133-146, 1988.
DOI : 10.1007/978-3-642-85966-3_12

G. Danoy, P. Bouvry, and O. Boissier, A Multi-Agent Organizational Framework for Coevolutionary Optimization, Transactions on Petri Nets and Other Models of Concurrency IV, number 6550 in Lecture Notes in Computer Science, pp.199-224, 2010.
DOI : 10.1007/978-3-642-18222-8_9

URL : https://hal.archives-ouvertes.fr/emse-00642758

P. Dayan and C. J. Watkins, Reinforcement Learning: A Computational Perspective, Encyclopedia of Cognitive Science, 2006.
DOI : 10.1002/0470018860.s00039

D. Leone, R. Festa, P. Marchitto, and E. , A Bus Driver Scheduling Problem: a??new mathematical model and a GRASP approximate solution, Journal of Heuristics, vol.1, issue.12, pp.441-466, 2011.
DOI : 10.1007/s10732-010-9141-3

J. Denzinger, M. Fuchs, and M. Fuchs, High performance ATP systems by combining several AI methods, Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI'97), pp.102-107, 1997.

M. Desrochers and J. Rousseau, Computer-aided transit scheduling: proceedings of the Fifth International Workshop on Computer-aided Scheduling of Public Transport, 1992.
DOI : 10.1007/978-3-642-85968-7

M. Desrochers and F. Soumis, A Column Generation Approach to the Urban Transit Crew Scheduling Problem, Transportation Science, vol.23, issue.1, pp.1-13, 1989.
DOI : 10.1287/trsc.23.1.1

T. G. Dias, J. P. De-sousa, C. , and J. F. , Genetic algorithms for the bus driver scheduling problem: a case study, Journal of the Operational Research Society, vol.53, issue.3, pp.324-335, 2002.
DOI : 10.1057/palgrave.jors.2601312

J. Dréo, J. Aumasson, W. Tfaili, and P. Siarry, ADAPTIVE LEARNING SEARCH, A NEW TOOL TO HELP COMPREHENDING METAHEURISTICS, International Journal on Artificial Intelligence Tools, vol.16, issue.03, pp.483-505, 2007.
DOI : 10.1142/S0218213007003370

M. El-abd and M. Kamel, A Taxonomy of Cooperative Search Algorithms, Hybrid Metaheuristics, number 3636 in Lecture Notes in Computer Science, pp.32-41, 2005.
DOI : 10.1007/11546245_4

A. Ernst, H. Jiang, M. Krishnamoorthy, and D. Sier, Staff scheduling and rostering: A review of applications, methods and models, European Journal of Operational Research, vol.153, issue.1, pp.3-27, 2004.
DOI : 10.1016/S0377-2217(03)00095-X

J. Ferber, Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, 1999.

J. Ferber and O. Gutknecht, A meta-model for the analysis and design of organizations in multi-agent systems, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160), pp.128-135, 1998.
DOI : 10.1109/ICMAS.1998.699041

J. Ferber, O. Gutknecht, M. , and F. , From Agents to Organizations: An Organizational View of Multi-agent Systems, Agent-Oriented Software Engineering IV, number 2935 in Lecture Notes in Computer Science, pp.214-230, 2004.
DOI : 10.1007/978-3-540-24620-6_15

URL : https://hal.archives-ouvertes.fr/lirmm-00269681

H. Fisher and G. Thompson, Probabilistic Learning Combinations of Local Job-Shop Scheduling Rules. Prentice-Hall international series in management, pp.380-387, 1963.

D. Fogel, L. Fogel, and J. Atmar, Meta-evolutionary programming, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers, pp.540-545, 1991.
DOI : 10.1109/ACSSC.1991.186507

S. Fores, Column Generation Approaches to Bus Driver Scheduling, 1996.

S. Fores, L. Proll, and A. Wren, Experiences with a Flexible Driver Scheduler, Computer-Aided Scheduling of Public Transport, number 505 in Lecture Notes in Economics and Mathematical Systems, pp.137-152, 2001.
DOI : 10.1007/978-3-642-56423-9_8

R. Freling, An Overview of Models and Techniques for Integrating Vehicle and Crew Scheduling, 1997.
DOI : 10.1007/978-3-642-85970-0_21

P. Garrido, C. Castro, and E. Monfroy, Towards a flexible and adaptable hyperheuristic approach for vrps, Proceedings of the 2009 International Conference on Artificial Intelligence Volumes, pp.311-317, 2009.

P. Garrido and M. Riff, DVRP: a hard dynamic combinatorial optimisation problem tackled by??an??evolutionary hyper-heuristic, Journal of Heuristics, vol.45, issue.10, pp.795-834, 2010.
DOI : 10.1007/s10732-010-9126-2

N. Gaud, Systèmes multi-agents holoniques: de l'analyse à l'implantation. Méta-modèle, méthodologie , et simulation multi-niveaux, 2007.

F. Glover, R. S. Barr, R. V. Helgason, and J. L. And-kennington, Tabu Search and Adaptive Memory Programming ??? Advances, Applications and Challenges, Interfaces in Computer Science and Operations Research, number 7 in Operations Research/Computer Science Interfaces Series, pp.1-75, 1997.
DOI : 10.1007/978-1-4615-4102-8_1

F. Glover and M. Laguna, Tabu search, Kluwer, 1998.
URL : https://hal.archives-ouvertes.fr/hal-01412610

J. Gratch and S. Chien, Learning search control knowledge for the deep space network scheduling problem, 1993.

P. Gruer, V. Hilaire, A. Koukam, C. , and K. , A formal framework for multi-agent systems analysis and design, Expert Systems with Applications, vol.23, issue.4, pp.349-355, 2002.
DOI : 10.1016/S0957-4174(02)00070-2

L. Han, G. Kendall, and P. Cowling, An adaptive length chromosome hyperheuristic genetic algorithm for a trainer scheduling problem, Proceedings of the fourth Asia-Pacific Conference on Simulated Evolution And Learning Orchid Country Club, pp.18-22, 2002.

M. Hannoun, O. Boissier, J. S. Sichman, and C. Sayettat, MOISE: An Organizational Model for Multi-agent Systems, Advances in Artificial Intelligence, number 1952 in Lecture Notes in Computer Science, pp.156-165, 2000.
DOI : 10.1007/3-540-44399-1_17

URL : https://hal.archives-ouvertes.fr/emse-00745232

B. Hayes-roth, An architecture for adaptive intelligent systems, Artificial Intelligence, vol.72, issue.1-2, pp.329-365, 1995.
DOI : 10.1016/0004-3702(94)00004-K

M. D. Hickman, P. B. Mirchandani, and S. Voss, Computer-aided systems in public transport, 2008.
DOI : 10.1007/978-3-540-73312-6

V. Hilaire, Vers une approche de spécification, de prototypage et de vérification de systèmes multiagents, 2000.

V. Hilaire, A. Koukam, P. Gruer, and J. Müller, Formal specification and prototyping of multiagent systems, Engineering Societies in the Agents World, number 1972 in Lecture Notes in Computer Science, pp.114-127, 2000.

J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 1992.

D. Huisman, R. Freling, and A. P. Wagelmans, Multiple-Depot Integrated Vehicle and Crew Scheduling, Transportation Science, vol.39, issue.4, pp.491-502, 2005.
DOI : 10.1287/trsc.1040.0104

A. Ieumwananonthachai and B. W. Wah, TEACHER ? an automated system for learning knowledgelean heuristics, 1995.

N. R. Jennings, K. Sycara, and M. Wooldridge, A roadmap of agent research and development, Autonomous Agents and Multi-Agent Systems, vol.1, issue.1, pp.7-38, 1998.
DOI : 10.1023/A:1010090405266

D. E. Joslin and D. P. Clements, Squeaky Wheel " optimization, Journal of Articial Intelligence Research, vol.10, issue.1, pp.353-373, 1999.

A. Kazemi, M. H. Zarandi, and S. M. Husseini, A multi-agent system to solve the production???distribution planning problem for a supply chain: a genetic algorithm approach, The International Journal of Advanced Manufacturing Technology, vol.15, issue.4, pp.180-193, 2009.
DOI : 10.1007/s00170-008-1826-5

R. Keller and R. Poli, Linear genetic programming of parsimonious metaheuristics, 2007 IEEE Congress on Evolutionary Computation, pp.4508-4515, 2007.
DOI : 10.1109/CEC.2007.4425062

R. E. Keller and R. Poli, Cost-Benefit Investigation of a Genetic-Programming Hyperheuristic, Artificial Evolution, number 4926 in Lecture Notes in Computer Science, pp.13-24, 2008.
DOI : 10.1007/978-3-540-79305-2_2

G. Kendall and N. Hussin, A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology, Practice and Theory of Automated Timetabling V, pp.270-293, 2005.
DOI : 10.1007/11593577_16

G. Kendall and M. Mohamad, Channel assignment in cellular communication using a great deluge hyper-heuristic, Proceedings. 2004 12th IEEE International Conference on Networks (ICON 2004) (IEEE Cat. No.04EX955), pp.769-773, 2004.
DOI : 10.1109/ICON.2004.1409283

B. Kiraz, A. Etaner-uyar, and E. Özcan, Selection hyper-heuristics in dynamic environments, Journal of the Operational Research Society, vol.12, issue.5, 2013.
DOI : 10.1007/978-3-540-49774-5

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

J. R. Koza, Genetic programming III: darwinian invention and problem solving, 1999.

A. Kwan, R. S. Kwan, M. E. Parker, and A. Wren, Producing train driver shifts by computer, Computer in Railways V, pp.421-435, 1996.
DOI : 10.1007/978-3-642-85970-0_7

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.1312

R. Kwan and A. Kwan, Effective search space control for large and/or complex driver scheduling problems, Annals of Operations Research, vol.6, issue.6, pp.417-435, 2007.
DOI : 10.1007/s10479-007-0203-3

G. Lan, G. W. Depuy, and G. E. Whitehouse, An effective and simple heuristic for the set covering problem, European Journal of Operational Research, vol.176, issue.3, pp.1387-1403, 2007.
DOI : 10.1016/j.ejor.2005.09.028

P. Leitão, J. Barbosa, and D. Trentesaux, Bio-inspired multi-agent systems for reconfigurable manufacturing systems, Engineering Applications of Artificial Intelligence, vol.25, issue.5, pp.934-944, 2012.
DOI : 10.1016/j.engappai.2011.09.025

J. Leung, Handbook of Scheduling: Algorithms, Models, and Performance Analysis, 2004.

J. Li, A Self-Adjusting Algorithm for Driver Scheduling, Journal of Heuristics, vol.22, issue.(3), pp.351-367, 2005.
DOI : 10.1007/s10732-005-2220-1

J. Li and R. S. Kwan, A fuzzy genetic algorithm for driver scheduling, European Journal of Operational Research, vol.147, issue.2, pp.334-344, 2003.
DOI : 10.1016/S0377-2217(02)00564-7

S. Li, A. H. Hassani, J. Créput, and A. Koukam, Heuristique de génération de colonnes pour l'habillage dans les systèmes de transport en commun, Actes du 14ème congrés national de la socitété française de recherche opérationnelle, 2013.

H. K. Lo, Proceedings of the 11th international conference on advanced systems for public transport, 2009.

E. López-camacho, H. Terashima-marín, R. , and P. , A hyper-heuristic for solving one and twodimensional bin packing problems, Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (GECCO'11), pp.257-258, 2011.

H. R. Lourenco, J. P. Paixao, and R. Portugal, Multiobjective Metaheuristics for the Bus Driver Scheduling Problem, Transportation Science, vol.35, issue.3, pp.331-343, 2001.
DOI : 10.1287/trsc.35.3.331.10147

M. E. Lübbecke and J. Desrosiers, Selected Topics in Column Generation, Operations Research, vol.53, issue.6, pp.1007-1023, 2005.
DOI : 10.1287/opre.1050.0234

P. Maes, Artificial life meets entertainment: lifelike autonomous agents, Communications of the ACM, vol.38, issue.11, pp.108-114, 1995.
DOI : 10.1145/219717.219808

B. Manington and A. Wren, A general computer method for bus crew scheduling, Proceedings of International Workshop on Urban Passenger Vehicle and Crew Scheduling, 1975.

G. R. Mauri and L. A. Lorena, A new hybrid heuristic for driver scheduling, International Journal of Hybrid Intelligent Systems, vol.4, issue.1, pp.39-47, 2007.
DOI : 10.3233/HIS-2007-4105

D. Meignan, Une approche organisationnelle et multi-agent pour la modélisation et l'implantation de métaheuristiques application aux problèmes d'optimisation de réseaux de transports, 2008.

D. Meignan, J. Créput, and A. Koukam, An organizational view of metaheuristics, Proceedings of the First International Workshop on Optimisation in Multi-Agent Systems, AAMAS'08, pp.77-85, 2008.

M. Meilton, Selecting and Implementing a Computer Aided Scheduling System for a Large Bus Company, pp.203-214, 2001.
DOI : 10.1007/978-3-642-56423-9_12

M. Milano and A. Roli, MAGMA: A Multiagent Architecture for Metaheuristics, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.2, pp.925-941, 2004.
DOI : 10.1109/TSMCB.2003.818432

S. Minton, Automatically configuring constraint satisfaction programs: A case study, Constraints, vol.58, issue.1, pp.7-43, 1996.
DOI : 10.1007/BF00143877

M. Misir, K. Verbeeck, P. De-causmaecker, and G. V. Berghe, Hyper-heuristics with a dynamic heuristic set for the home care scheduling problem, IEEE Congress on Evolutionary Computation, pp.1-8, 2010.
DOI : 10.1109/CEC.2010.5586348

G. Mitra and A. P. Welsh, A computer based crew scheduling system using amathematical programming approach, Proceedings of the International Conference on Computer Scheduling of Public Transport, pp.281-296, 1981.

A. Nareyek, Choosing Search Heuristics by Non-Stationary Reinforcement Learning, Metaheuristics: Computer Decision-Making, number 86 in Applied Optimization, pp.523-544, 2004.
DOI : 10.1007/978-1-4757-4137-7_25

G. Ochoa, R. Qu, and E. K. Burke, Analyzing the landscape of a graph based hyper-heuristic for timetabling problems, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, GECCO '09, pp.341-348, 2009.
DOI : 10.1145/1569901.1569949

G. Ochoa, J. A. Vázquez-rodríguez, S. Petrovic, and E. Burke, Dispatching rules for production scheduling: A hyper-heuristic landscape analysis, 2009 IEEE Congress on Evolutionary Computation, pp.1873-1880, 2009.
DOI : 10.1109/CEC.2009.4983169

D. Ouelhadj and S. Petrovic, A cooperative distributed Hyper-Heuristic framework for scheduling, 2008 IEEE International Conference on Systems, Man and Cybernetics, pp.2560-2565, 2008.
DOI : 10.1109/ICSMC.2008.4811681

D. Ouelhadj and S. Petrovic, A cooperative hyper-heuristic search framework, Journal of Heuristics, vol.5, issue.2, pp.835-857, 2009.
DOI : 10.1007/s10732-009-9122-6

E. Özcan, B. Bilgin, and E. E. Korkmaz, A comprehensive analysis of hyper-heuristics, Intell. Data Anal, vol.12, issue.1, pp.3-23, 2008.

E. Özcan, M. Misir, and E. K. Burke, A self-organising hyper-heuristi framework, Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA 09), pp.790-797, 2009.

E. Özcan, M. Misir, G. Ochoa, and E. K. Burke, A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling, International Journal of Applied Metaheuristic Computing, vol.1, issue.1, pp.39-59, 2010.
DOI : 10.4018/jamc.2010102603

L. Panait and S. Luke, Cooperative Multi-Agent Learning: The State of the Art, Autonomous Agents and Multi-Agent Systems, vol.4, issue.2-3, pp.387-434, 2005.
DOI : 10.1007/s10458-005-2631-2

M. E. Parker and B. M. Smith, Two approaches to computer crew scheduling computer scheduling of public transport, Proceedings of the International Conference on Computer Scheduling of Public Transport, pp.193-222, 1981.

I. Rechenberg and . Tu-berlin, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, 1971.

I. Rechenberg and H. P. Schwefel, Adaptive Mechanismen in der Biologischen Evolution, 1974.

P. Ross, Hyper-heuristics, Search Methodologies, pp.529-556, 2005.

S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2003.

D. M. Ryan and B. A. Foster, An integer programming approach to scheduling, Proceedings of the International Conference on Computer Scheduling of Public Transport, pp.269-280, 1981.

Y. Shen and R. S. Kwan, Tabu Search for Driver Scheduling, Computer-Aided Scheduling of Public Transport, pp.121-135, 2001.
DOI : 10.1007/978-3-642-56423-9_7

F. Shepardson, Modeling the bus crew scheduling problem. Amsterdam The Netherlands, 1981.

K. Sim, E. Hart, B. D. Paechter, T. Kanade, J. Kittler et al., A Hyper-Heuristic Classifier for One Dimensional Bin Packing Problems: Improving Classification Accuracy by Attribute Evolution, Parallel Problem Solving from Nature -PPSN XII, pp.348-357, 2012.
DOI : 10.1007/978-3-642-32964-7_35

S. Singh, T. Jaakkola, M. L. Littman, and C. Szepesvári, Convergence results for single-step onpolicy reinforcement-learning algorithms, MACHINE LEARNING, pp.287-308, 1998.

D. C. Smith, A. Cypher, and J. Spohrer, KidSim: programming agents without a programming language, Communications of the ACM, vol.37, issue.7, pp.54-67, 1994.
DOI : 10.1145/176789.176795

E. Soubeiga, Development and Application of Hyper-heuristics to Personnel Scheduling, 2003.

P. Stone and M. Veloso, Multiagent systems: A survey from a machine learning perspective, Autonomous Robots, vol.8, issue.3, pp.345-383, 2000.
DOI : 10.1023/A:1008942012299

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

G. Taillard, L. M. Gendreau, M. Potvin, and J. , Adaptive memory programming: A unified view of metaheuristics, European Journal of Operational Research, vol.135, issue.1, pp.1-16, 2001.
DOI : 10.1016/S0377-2217(00)00268-X

R. Tanese, Distributed genetic algorithms, International Conference on Genetic Algorithms, pp.434-439, 1989.

J. Vázquez-rodríguez and S. Petrovic, A new dispatching rule based genetic algorithm for??the??multi-objective job shop problem, Journal of Heuristics, vol.7, issue.2, pp.771-793, 2010.
DOI : 10.1007/s10732-009-9120-8

S. Voss and J. R. Daduna, Computer-aided scheduling of public transport, 2001.

C. Watkins, Learning from Delayed Rewards, 1989.

G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, 1999.

D. Wolpert and W. 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

A. Wren and M. Chamberlain, The development of micro-BUSMAN: scheduling on microcomputers, Computer-Aided Transit Scheduling, number 308 in Lecture Note in Economics Mathematical Systems, pp.160-174, 1988.

A. Wren and J. Rousseau, Bus Driver Scheduling ??? An Overview, 1995.
DOI : 10.1007/978-3-642-57762-8_12

A. Wren and B. M. Smith, Experiences with a crew schduling system based on set covering. From the book Computer-aided transit scheduling, 1988.

T. Yamaguchi, Y. Tanaka, Y. , and M. , Speed up reinforcement learning between two agents with adaptive mimetism, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97, pp.594-600, 1997.
DOI : 10.1109/IROS.1997.655072

B. Zhao, C. Guo, C. , and Y. , A Multiagent-Based Particle Swarm Optimization Approach for Optimal Reactive Power Dispatch, IEEE Transactions on Power Systems, vol.20, issue.2, pp.1070-1078, 2005.
DOI : 10.1109/TPWRS.2005.846064

L. Zhao, A heuristic method for analyzing driver scheduling problem, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.36, issue.3, pp.521-531, 2006.
DOI : 10.1109/TSMCA.2005.853497