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Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York (1966)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart (1973)
Schwefel, H.P.: Kybernetische Evolution als Strategie der experimentellen Forschung in der Strömungstechnik. Diplomarbeit, Technische Universität Berlin, Hermann Föttinger-Institut für Strömungstechnik (1965)
Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated an- nealing. Science 220 (1983) 671-680
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston, MA (1997)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1) (1997) 67-82
Hart, W.E., Belew, R.K.: Optimizing an arbitrary function is hard for the genetic algorithm. In Belew, R.K., Booker, L.B., eds.: Proceedings of the 4th International Conference on Genetic Algorithms, San Mateo CA, Morgan Kaufmann (1991) 190-195
Davis, L.D.: Handbook of Genetic Algorithms. Van Nostrand Reinhold Computer Library, New York (1991)
Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Mar- tial Arts: Towards Memetic Algorithms. Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA (1989)
Moscato, P.: Memetic algorithms: A short introduction. In Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization. McGraw-Hill, Maidenhead, Berkshire, England, UK (1999) 219-234
Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In Glover, F., Kochenberger, G., eds.: Handbook of Metaheuristics. Kluwer Academic Publishers, Boston MA (2003) 105-144
Moscato, P., Cotta, C., Mendes, A.S.: Memetic algorithms. In Onwubolu, G.C., Babu, B.V., eds.: New Optimization Techniques in Engineering. Springer-Verlag, Berlin Heidelberg (2004) 53-85
Dawkins, R.: The Selfish Gene. Clarendon Press, Oxford (1976)
Culberson, J.: On the futility of blind search: An algorithmic view of “No Free Lunch”. Evolutionary Computation 6 (1998) 109-127
Eiben, A.E., Raue, P.E., Ruttkay, Z.: Genetic algorithms with multi-parent re- combination. In Davidor, Y., Schwefel, H.P., Männer, R., eds.: Parallel Problem Solving From Nature III. Volume 866 of Lecture Notes in Computer Science. Springer-Verlag (1994) 78-87
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA (1989)
Jones, T.C.: Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico (1995)
Davidor, Y., Ben-Kiki, O.: The interplay among the genetic algorithm opera- tors: Information theory tools used in a holistic way. In Männer, R., Manderick, B., eds.: Parallel Problem Solving From Nature II, Amsterdam, Elsevier Science Publishers B.V. (1992) 75-84
Radcliffe, N.J.: Non-linear genetic representations. In Männer, R., Manderick, B., eds.: Parallel Problem Solving From Nature II, Amsterdam, Elsevier Science Publishers B.V. (1992) 259-268
Fox, B.R., McMahon, M.B.: Genetic operators for sequencing problems. In Rawl- ins, G.J.E., ed.: Foundations of Genetic Algorithms I, San Mateo, CA, Morgan Kaufmann (1991) 284-300
Cotta, C., Troya, J.M.: Genetic forma recombination in permutation flowshop problems. Evolutionary Computation 6 (1998) 25-44
Mathias, K., Whitley, L.D.: Genetic operators, the fitness landscape and the traveling salesman problem. In Männer, R., Manderick, B., eds.: Parallel Problem Solving From Nature II, Amsterdam, Elsevier Science Publishers B.V. (1992) 221- 230
Goldberg, D.E., Lingle Jr., R.: Alleles, loci and the traveling salesman problem. In Grefenstette, J.J., ed.: Proceedings of the 1st International Conference on Genetic Algorithms, Hillsdale NJ, Lawrence Erlbaum Associates (1985) 154-159
Davidor, Y.: Epistasis Variance: Suitability of a Representation to Genetic Algorithms. Complex Systems 4 (1990) 369-383
Davidor, Y.: Epistasis variance: A viewpoint on GA-hardness. In Rawlins, G.J.E., ed.: Foundations of Genetic Algorithms I, San Mateo, CA, Morgan Kaufmann (1991)23-35
Radcliffe, N.J., Surry, P.D.: Fitness Variance of Formae and Performance Predic- tion. In Whitley, L.D., Vose, M.D., eds.: Foundations of Genetic Algorithms III, San Francisco, CA, Morgan Kaufmann (1994) 51-72
Manderick, B., de Weger, M., Spiessens, P.: The Genetic Algorithm and the Struc- ture of the Fitness Landscape. In Belew, R.K., Booker, L.B., eds.: Proceedings of the 4th International Conference on Genetic Algorithms, San Mateo, CA, Morgan Kaufmann (1991) 143-150
Dzubera, J., Whitley, L.D.: Advanced Correlation Analysis of Operators for the Traveling Salesman Problem. In Schwefel, H.P., Männer, R., eds.: Parallel Prob- lem Solving from Nature III. Volume 866 of Lecture Notes in Computer Science., Dortmund, Germany, Springer-Verlag, Berlin, Germany (1994) 68-77
Aickelin, U., Dowsland, K.: Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem. Journal of Scheduling 3 (2000) 139-153
Puente, J., Vela, C.R., Prieto, C., Varela, R.: Hybridizing a genetic algorithm with local search and heuristic seeding. In Mira, J., Álvarez, J.R., eds.: Artificial Neural Nets Problem Solving Methods. Volume 2687 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2003) 329-336
Varela, R., Serrano, D., Sierra, M.: New codification schemas for scheduling with genetic algorithms. In Mira, J., Á lvarez, J.R., eds.: Artificial Intelligence and Knowledge Engineering Applications: a Bioinspired Approach. Volume 3562 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2005) 11-20
Varela, R., Puente, J., Vela, C.R.: Some issues in chromosome codification for scheduling with genetic algorithms. In Castillo, L., Borrajo, D., Salido, M.A., Oddi, A., eds.: Planning, Scheduling and Constraint Satisfaction: From Theory to Practice. Volume 117 of Frontiers in Artificial Intelligence and Applications. IOS Press (2005) 1-10
Radcliffe, N.J.: The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence 10 (1994) 339-384
Oğuz, C., Ercan, M.F.: A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks. Journal of Scheduling 8 (2005) 323-351
Cotta, C., Troya, J.M.: Embedding branch and bound within evolutionary algorithms. Applied Intelligence 18 (2003) 137-153
Ibaraki, T.: Combination with dynamic programming. In Bäck, T., Fogel, D., Michalewicz, Z., eds.: Handbook of Evolutionary Computation. Oxford University Press, New York NY (1997) D3.4:1-2
Yamada, T., Nakano, R.: A genetic algorithm with multi-step crossover for job- shop scheduling problems. In: Proceedings of the 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK, Institution of Electrical Engineers (1995) 146-151
Wang, L., Zheng, D.Z.: A modified genetic algorithm for job-shop scheduling. International Journal of Advanced Manufacturing Technology 20 (2002) 72-76
Maheswaran, R., Ponnambalam, S.G., Aranvidan, C.: A meta-heuristic approach to single machine scheduling problems. International Journal of Advanced Manufacturing Technology 25 (2005) 772-776
Liaw, C.F.: A hybrid genetic algorithm for the open shop scheduling problem. European Journal of Operational Research 124 (2000) 28-42
Sevaux, M., Dauzère-Pérès, S.: Genetic algorithms to minimize the weighted number of late jobs on a single machine. European Journal of Operational Research 151 (2003)296-306
Burke, E.K., Newall, J., Weare, R.: A memetic algorithm for university exam timetabling. In Burke, E.K., Ross, P., eds.: Practice and Theory of Automated Timetabling. Volume 1153 of Lecture Notes in Computer Science., Berlin Heidel- berg, Springer-Verlag (1996)
Burke, E.K., Smith, A.J.: A memetic algorithm to schedule planned grid main- tenance. In Mohammadian, M., ed.: Computational Intelligence for Modelling, Control and Automation, IOS Press (1999) 12-127
França, P.M., Gupta, J.N.D., Mendes, A.S., Moscato, P., Veltnik, K.J.: Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups. Computers and Industrial Engineering 48 (2005) 491- 506
Hansen, P., Mladenović, N.: Variable neighborhood search: Principles and appli- cations. European Journal of Operational Research 130 (2001) 449-467
Yeh, W.C.: A memetic algorithm fo the n/2/Flowshop/αF+ βCmax scheduling problem. International Journal of Advanced Manufacturing Technology 20 (2002) 464-473
Varela, R., Gómez, A., Vela, C.R., Puente, J., Alonso, C.: Heuristic generation of the initial population in solving job shop problems by evolutionary strategies. In Mira, J., Sánchez-Andrés, J.V., eds.: Foundations and Tools for Neural Modeling. Volume 1606 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer- Verlag (1999) 690-699
Varela, R., Puente, J., Vela, C.R., Gómez, A.: A knowledge-based evolutionary strategy for scheduling problems with bottlenecks. European Journal of Opera- tional Research 145 (2003) 57-71
Burke, E.K., Petrovic, S.: Recent research directions in automated timetabling. European Journal of Operational Research 140 (2002) 266-280
Rossi-Doria, O., Paechter, B.: A memetic algorithm for university course timetabling. In: Combinatorial Optimisation 2004 Book of Abstracts, Lancaster, UK, Lancaster University (2004) 56
Laguna, M., Martí, R.: Scatter Search. Methodology and Implementations in C. Kluwer Academic Publishers, Boston MA (2003)
Nagata, Y., Kobayashi, S.: Edge assembly crossover: A high-power genetic algo- rithm for the traveling salesman problem. In Bäck, T., ed.: Proceedings of the Seventh International Conference on Genetic Algorithms, East Lansing, EE.UU., San Mateo, CA, Morgan Kaufmann (1997) 450-457
Yamada, T.>, Reeves, C.R.: Solving the Csum permutation flowshop scheduling problem by genetic local search. In: 1998 IEEE International Conference on Evolutionary Computation, Piscataway, NJ, IEEE Press (1998) 230-234
Ž d ánský, M., Poživil, J.: Combination genetic/tabu search algorithm for hybrid flowshops optimization. In: Proceedings of ALGORITMY 2002 - Conference on Scientific Computing, Vysoke Tatry, Podbanske, Slovakia (2002) 230-236
Costa, D.: An evolutionary tabu search algorithm and the NHL scheduling problem. INFOR 33 (1995) 161-178
Greistorfer, P.: Hybrid genetic tabu search for a cyclic scheduling problem. In Voß, S., Martello, S., Osman, I.H., Roucairol, C., eds.: Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer Academic Publishers, Boston, MA (1998) 213-229
Greistorfer, P.: Genetic tabu search extensions for the solving of the cyclic reg- ular max-min scheduling problem. In: International Conference on Operations Research (OR98), Zürich, Schwitzerland (1998)
Burke, E.K., Cowling, P.I., De Causmaecker, P., van den Berghe, G.: A memetic approach to the nurse rostering problem. Applied Intelligence 15 (2001) 199-214
Batenburg, K.J., Palenstijn, W.J.: A new exam timetabling algorithm. In Hes- kes, T., Lucas, P., Vuurpijl, L., Wiegerinck, W., eds.: Proceedings of the 15th Belgian-Dutch Conference on Artificial Intelligence BNAIC’03, Nijmegen, The Netherlands (2003) 19-26
Digalakis, J., Margaritis, K.: A multipopulation memetic model for the mainte- nance scheduling problem. In: Proceedings of the 5th Hellenic European Confer- ence on Computer Mathematics and its Applications, Athens, Greece, LEA Press (2001)318-323
Burke, E.K., Smith, A.J.: A memetic algorithm to schedule planned maintenance for the national grid. Journal of Experimental Algorithmics 4 (1999) 1-13
Ponnambalam, S.G., Mohan Reddy, M.: A GA-SA multiobjective hybrid search algorithm for integrating lot sizing and sequencing in flow-line scheduling. International Journal of Advanced Manufacturing Technology 21 (2003) 126-137
Houck, C., Joines, J.A., Kay, M.G., Wilson, J.R.: Empirical investigation of the benefits of partial lamarckianism. Evolutionary Computation 5 (1997) 31-60
Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 7 (2003) 204-223
Jones, T.C., Forrest, S.: Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Eshelman, L.J., ed.: Proceedings of the 6th International Conference on Genetic Algorithms, Morgan Kaufmann (1995) 184- 192
Merz, P., Freisleben, B.: Fitness landscapes and memetic algorithm design. In Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization. McGraw-Hill, Maidenhead, Berkshire, England, UK (1999) 245-260
English, T.M.: Evaluation of evolutionary and genetic optimizers: No free lunch. In Fogel, L.J., Angeline, P.J., Bäck, T., eds.: Evolutionary Programming V, Cambridge, MA, MIT Press (1996) 163-169
Bierwirth, C., Mattfeld, D.C., Watson, J.P.: Landscape regularity and random walks for the job shop scheduling problem. In Gottlieb, J., Raidl, G.R., eds.: Evo- lutionary Computation in Combinatorial Optimization. Volume 3004 of Lecture Notes in Computer Science., Berlin, Springer-Verlag (2004) 21-30
Grefenstette, J.J.: Genetic algorithms for changing environments. In Männer, R., Manderick, B., eds.: Parallel Problem Solving from Nature II, Amsterdam, North-Holland Elsevier (1992) 137-144
Hadj-Alouane, A.B., Bean, J.C., Murty, K.G.: A hybrid genetic/optimization algorithm for a task allocation problem. Journal of Scheduling 2 (1999) 181-201
Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Springer-Verlag (2005)
França, P.M., Mendes, A.S., Müller, F., Moscato, P.: Memetic algorithms ap- plied to the single machine and parallel machine scheduling problems. In: Anais da Primeira Oficina de Planejamento e Controle da Produção em Sistemas de Manufatura, Campinas, SP, Brazil (1999)
França, P.M., Mendes, A.S., Moscato, P.: Memetic algorithms to minimize tar- diness on a single machine with sequence-dependent setup times. In Despotis, D.K., Zopounidis, C., eds.: Proceedings of the 5th International Conference of the Decision Sciences Institute, Athens, Greece (1999) 1708-1710
Mendes, A.S., Müller, F., França, P.M., Moscato, P.: Comparing meta-heuristic approaches for parallel machine scheduling problems with sequence-dependent setup times. In: Procedings of the 15th International Conference on CAD/CAM Robotics and Factories of the Future, Á guas de Lindóia, SP, Brazil (1999) 1-6
França, P.M., Gupta, J.N.D., Mendes, A.S., Moscato, P., Veltnik, K.J.: Meta- heuristic approaches for the pure flowshop manufacturing cell problem. In: Pro- ceedings of the 7th International Workshop on Project Management and Schedul- ing, Osnabrück, Germany (2000) 128-130
Mendes, A.S., França, P.M., Moscato, P.: Fuzzy-evolutionary algorithms applied to scheduling problems. In: Proceedings of the 1st World Conference on Production and Operations Management, Seville, Spain (2000) 1-10
França, P.M., Mendes, A.S., Moscato, P.: A memetic algorithm for the total tardiness single machine scheduling problem. European Journal of Operational Research 132 (2001) 224-242
Mendes, A.S., França, P.M., Moscato, P.: Fitness landscapes for the total tardiness single machine scheduling problem. Neural Network World 2 (2002) 165-180
Moscato, P., Mendes, A., Cotta, C.: Scheduling and production & control. In Onwubolu, G.C., Babu, B.V., eds.: New Optimization Techniques in Engineering. Springer-Verlag, Berlin Heidelberg (2004) 655-680
Cotta, C., Alba, E., Troya, J.M.: Stochastic reverse hillclimbing and iterated local search. In: Proceedings of the 1999 Congress on Evolutionary Computation, Washington D.C., IEEE Neural Network Council - Evolutionary Programming Society - Institution of Electrical Engineers (1999) 1558-1565
Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington DC (1990)
Krasnogor, N.: Studies on the Theory and Design Space of Memetic Algorithms. PhD thesis, Faculty of Engineering, Computer Science and Mathematics. University of the West of England. Bristol, United Kingdom (2002)
Sevaux, M., Sörensen, K.: A genetic algorithm for robust schedules. In: Proceedings of the 8th International Workshop on Project Management and Scheduling. (2002)330-333
Cheng, R., Gen, M.: Parallel machine scheduling problems using memetic algorithms. Computers and Industrial Engineering 33 (1997) 761-764
Bonfim, T.R., Yamakami, A.: Neural network applied to the coevolution of the memetic algorithm for solving the makespan minimization problem in parallel machine scheduling. In Ludermir, T.B., de Souto, M.C.P., eds.: Proceedings of the 7th Brazilian Symposium on Neural Networks (SBRN 2002), Recife, Brazil, IEEE Computer Society (2002) 197-199
Yamada, T., Reeves, C.R.: Permutation flowshop scheduling by genetic local search. In: Proceedings of the 2nd International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, London, UK, Institution of Electrical Engineers (1997) 232-238
Reeves, C.R., Yamada, T.: Genetic algorithms, path relinking and the flowshop sequencing problem. Evolutionary Computation 6 (1998) 230-234
Glover, F.: Scatter search and path relinking. In Corne, D., Dorigo, M., Glover, F., eds.: New Methods in Optimization. McGraw-Hill, London (1999) 291-316
Sevaux, M., Jouglet, A., Oğuz, C.: MLS+CP for the hybrid flowshop scheduling problem. In: Workshop on the Combination of metaheuristic and local search with Constraint Programming techniques, Nantes, France (2005)
Sevaux, M., Jouglet, A., Oğuz, C.: Combining constraint programming and memetic algorithm for the hybrid flowshop scheduling problem. In: ORBEL 19th annual conference of the SOGESCI-BVWB, Louvain-la-Neuve, Belgium (2005)
Yamada, T., Nakano, R.: A fusion of crossover and local search. In: IEEE International Conference on Industrial Technology ICIT’96, Shangai, China, IEEE Press (1996) 426-430
Yamada, T., Nakano, R.: Scheduling by genetic local search with multi-step crossover. In Voigt, H.M., Ebeling, W., Rechenberg, I., Schwefel, H.P., eds.: Parallel Problem Solving From Nature IV. Volume 1141 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (1996) 960-969
Wang, L., Zheng, D.Z.: An effective hybrid optimization strategy for job-shop scheduling problems. Computers & Operations Research 28 (2001) 585-596
Paechter, B., Cumming, A., Luchian, H.: The use of local search suggestion lists for improving the solution of timetable problems with evolutionary algorithms. In Fogarty, T.C., ed.: AISB Workshop on evolutionary computing. Volume 993 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (1995) 86-93
Rankin, B.: Memetic timetabling in practice. In Burke, E.K., Ross, P., eds.: Proceedings of the 1st International Conference on the Practice and Theory of Automated Timetabling. Volume 1153 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (1996) 266-279
Paechter, B., Cumming, A., Norman, M.G., Luchian, H.: Extensions to a memetic timetabling system. In Burke, E.K., Ross, P., eds.: Proceedings of the 1st Interna- tional Conference on the Practice and Theory of Automated Timetabling. Volume 1153 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (1996)251-265
Burke, E.K., Newall, J.P.: Investigating the benefits of utilising problem specific heuristics within a memetic timetabling algorithm. Workin Paper NOTTCS-TR- 97-6, dept. of Computer Science, University of Nottingham, UK (1997)
Burke, E.K., Newall, J.: A multi-stage evolutionary algorithm for the timetable problem. IEEE Transactions on Evolutionary Computation 3 (1999) 63-74
Alkan, A., Ö zcan, E.: Memetic algorithms for timetabling. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, Canberra, Australia, IEEE Press (2003) 1796-1802
Wilke, P., Gröbner, M., Oster, N.: A hybrid genetic algorithm for school timetabling. In McKay, B., Slaney, J., eds.: AI 2002: Advances in Artificial Intelli- gence, 15th Australian Joint Conference on Artificial Intelligence. Volume 2557 of Lecture Notes in Computer Science., Canberra, Australia, Springer (2002) 455- 464
Burke, E.K., Jackson, K., Kingston, J.H., Weare, R.F.: Automated university timetabling: The state of the art. The Computer Journal 40 (1997) 565-571
Burke, E.K., Landa Silva, J.D.: The design of memetic algorithms for scheduling and timetabling problems. In Krasnogor, N., Hart, W., Smith, J., eds.: Recent Advances in Memetic Algorithms. Volume 166 of Studies in Fuzziness and Soft Computing. Springer-Verlag (2004) 289-312
Petrovic, S., Burke, E.K.: University timetabling. In Leung, J., ed.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Chapman Hall/CRC Press (2004)
Semet, Y., Schoenauer, M.: An efficient memetic, permutation-based evolutionary algorithm for real-world train timetabling. In: Proceedings of the 2005 Congress on Evolutionary Computation, Edinburgh, UK, IEEE Press (2005) 2752-2759
Hertz, A., de Werra, D.: Using tabu search techniques for graph coloring. Computing 39 (1987) 345-351
Costa, D.: On the use of some known methods for T-colorings of graphs. Annals of Operations Research 41 (1993) 343-358
Schönberger, J., Mattfeld, D.C., Kopfer, H.: Memetic algorithm timetabling for non-commercial sport leagues. European Journal of Operational Research 153 2004102-116
Aickelin, U.: Nurse rostering with genetic algorithms. In: Proceedings of young operational research conference 1998, Guildford, UK (1998)
De Causmaecker, P., van den Berghe, G.: Using tabu search as a local heuristic in a memetic algorithm for the nurse rostering problem. In: Proceedings of the 13th Conference on Quantitative Methods for Decision Making (ORBEL XIII), Brussels (1999)
Gröbner, M., Wilke, P.: Optimizing employee schedules by a hybrid genetic algorithm. In Boers, E.J.W., et al., eds.: Applications of Evolutionary Computing 2001. Volume 2037 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2001) 463-472
Burke, E.K., De Causmaecker, P., van den Berghe, G.: Novel metaheuristic approaches to nurse rostering problems in belgian hospitals. In Leung, J., ed.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Chapman Hall/CRC Press (2004) 44.1-44.18
Ö zcan, E.: Memetic algorithms for nurse rostering. In Yolum, P., Güngör, T., Gürgen, F.S., Ö zturan, C.C., eds.: Computer and Information Sciences - ISCIS 2005, 20th International Symposium (ISCIS). Volume 3733 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2005) 482-492
Li, J., Kwan, R.S.K.: A fuzzy genetic algorithm for driver scheduling. European Journal of Operational Research 147 (2003) 334-344
Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6 (1995) 109-133
Li, J., Kwan, R.S.K.: A self adjusting algorithm for driver scheduling. Journal of Heuristics 11 (2005) 351-367
Burke, E.K., Smith, A.J.: Hybrid evolutionary techniques for the maintenance scheduling problem. IEEE Transactions on Power Systems 15 (2000) 122-128
Burke, E.K., Smith, A.J.: A memetic algorithm for the maintenance scheduling problem. In: Proceedings of the 4th International Conference on Neural Infor- mation Processing ICONIP’97, Dunedin, New Zealand, Springer-Verlag (1997) 469-474
Burke, E., Clark, J., Smith, J.: Four methods for maintenance scheduling. In Smith, G., Steele, N., Albrecht, R., eds.: Artificial Neural Nets and Genetic Algorithms 3, Wien New York, Springer-Verlag (1998) 264-269
Evans, S., Fletcher, I.: A variation on a memetic algorithm for boiler scheduling. In Hotz, L., Krebs, T., eds.: Proceedings Workshop Planen und Konfigurieren (PuK-2003), Hamburg, Germany (2003)
Valenzuela, J., Smith, A.: A seeded memetic algorithm for large unit commitment problems. Journal of Heuristics 8 (2002) 173-195
Marriot, K., Stuckey, P.J.: Programming with constraints. The MIT Press, Cambridge, Massachusetts (1998)
Smith, B.M.: A tutorial on constraint programming. Research Report 95.14, University of Leeds, School of Computer Studies, England (1995)
Dechter, R.: Constraint processing. Morgan Kaufmann (2003)
Apt, K.R.: Principles of constraint programming. Cambridge University Press (2003)
Frühwirth, T., Abdennadher, S.: Essentials of constraint programming. Cognitive Technologies Series. Springer-Verlag (2003)
Tsang, E.: Foundations of constraint satisfaction. Academic Press, London and San Diego (1993)
Larrosa, J., Morancho, E., Niso, D.: On the practical use of variable elimination in constraint optimization problems: ‘still life’ as a case study. Journal of Artificial Intelligence Research 23 (2005) 421-440
Baptiste, P., Le Pape, C.: Constraint propagation and decomposition techniques for highly disjunctive and highly cumulative project scheduling problems. Constraints 5 (2000) 119-139
Barták, R.: Constraint satisfaction for planning and scheduling. In Vlahavas, I., Vrakas, D., eds.: Intelligent Techniques for Planning. Idea Group, Hershey, PA, USA (2005)
Le Pape, C.: Constraint-based scheduling: A tutorial. Proceedings of the 1st International Summer School on Constraint Programming (2005)
Kilby, P., Prosser, P., Shaw, P.: A comparison of traditional and constraint-based heuristic methods on vehicle routing problems with side constraints. Constraints 5 (2000)389-414
Oliveira, E., Smith, B.M.: A combined constraint-based search method for single-track railway scheduling problem. In Brazdil, P., Jorge, A., eds.: Proceedings of the 10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving. Volume 2258 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2001) 371-378
Wallace, R.J., Freuder, E.C.: Supporting dispatchability in schedules with consumable resources. Journal of Scheduling 8 (2005) 7-23
Khemmoudj, M.O.I., Porcheron, M., Bennaceur, H.: Using constraint programming and local search for scheduling of electricité de france nuclear power plant outages. In: Proceedings of the Workshop on the Combination of Metaheuris- tic and Local Search with Constraint Programming techniques, Nantes, France (2005)
Bistarelli, S.: Semirings for Soft Constraint Solving and Programming. Volume 2962 of Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg (2004)
Rossi, F.: Preference reasoning. In van Beek, P., ed.: Proceedings of the 11th International Conference on Principles and Practice of Constraint Programming. Volume 3709 of Lecture Notes in Computer Science., Berlin Heidelberg, SpringerVerlag (2005) 9-12
Backer, B.D., Furnon, V., Shaw, P., Kilby, P., Prosser, P.: Solving vehicle routing problems using constraint programming and metaheuristics. Journal of Heuristics 6 (2000)501-523
Yun, Y.S., Gen, M.: Advanced scheduling problem using constraint programming techniques in SCM environment. Computers & Industrial Engineering 43 (2002) 213-229
Merlot, L.T.G., Boland, N., Hughes, B.D., Stuckey, P.J.: A hybrid algorithm for the examination timetabling problem. In Burke, E.K., Causmaecker, P.D., eds.: Proceedings of the 4th International Conference on the Practice and Theory of Automated Timetabling. Volume 2740 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2003) 207-231
Terashima, H.: Combinations of GAs and CSP strategies for solving examination timetabling problems. PhD thesis, Instituto Tecnológico y de Estudios Superiores de Monterrey (1998)
Landa Silva, J.D., Burke, E.K., Petrovic, S.: An introduction to multiobjective metaheuristics for scheduling and timetabling. In X., G., M., S., Sörensen K. and, T.V., eds.: Metaheuristic for Multiobjective Optimisation. Volume 535 of Lecture Notes in Economics and Mathematical Systems. Springer (2004) 91-129
Krasnogor, N., Smith, J.E.: Emergence of profitable search strategies based on a simple inheritance mechanism. In Spector, L., et al., eds.: Proceedings of the 2001 Genetic and Evolutionary Computation Conference, Morgan Kaufmann (2001) 432-439
Kendall, G., Soubeiga, E., Cowling, P.I.: Choice function and random hyperheuristics. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL’02). (2002) 667-671
Burke, E.K., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyperheuristics: an emerging direction in modern search technology. In Glover, F., Kochenberger, G., eds.: Handbook of Metaheuristics. Kluwer Academic Publishers, Boston MA (2003) 457-474
Cowling, P.I., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In Burke, E., Erben, W., eds.: Selected Papers of the 3rd PATAT conference. Volume 2079 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2000) 176-190
Cowling, P.I., Kendall, G., Soubeiga, E.: Hyperheuristics: A tool for rapid proto-typing in scheduling and optimisation. In Cagnoni, S., et al., eds.: Applications of Evolutionary Computing. Volume 2279 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2002) 1-10
Cowling, P.I., Kendall, G., Soubeiga, E.: Hyperheuristics: A robust optimisation method applied to nurse scheduling. In Merelo, J.J., et al., eds.: Parallel Problem Solving from Nature VII. Volume 2439 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2002) 851-860
Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics 9 (2003) 451-470
Burke, E.K., McCollum, B., Meisels, A., Petrovic, S., Qu, R.: A graph-based hyper-heuristic for educational timetabling problems. European Journal of Operational Research (2006) In press.
Cowling, P.I., Ouelhadj, D., Petrovic, S.: Dynamic scheduling of steel casting and milling using multi-agents. Production Planning and Control 15 (2002) 1-11
Cowling, P.I., Ouelhadj, D., Petrovic, S.: A multi-agent architecture for dynamic scheduling of steel hot rolling. Journal of Intelligent Manufacturing 14 (2002) 457-470
Ouelhadj, D., Petrovic, S., Cowling, P.I., Meisels, A.: Inter-agent cooperation and communication for agent-based robust dynamic scheduling in steel production. Advanced Engineering and Informatics 18 (2005) 161-172
Cotta, C., Moscato, P.: Evolutionary computation: Challenges and duties. In Menon, A., ed.: Frontiers of Evolutionary Computation. Kluwer Academic Publishers, Boston MA (2004) 53-72
Barnier, N., Brisset, P.: Solving Kirkman’s schoolgirl problem in a few seconds. Constraints 10 (2005) 7-21
Dotú, I., del Val, A., van Hentenryck, P.: Scheduling social tournaments. In van Beek, P., ed.: Proceedings of the 11th International Conference on Principles and Practice of Constraint Programming. Volume 3709 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2005) 845
Cheng, T.C.E., Diamond, J.: Optimal scheduling in film production to minimize talent hold cost. Journal of Optimization Theory and Applications 79 (1993) 197-206
Smith, B.M.: Constraint programming in practice: scheduling a rehearsal. Re- search Report APES-67-2003, APES group (2003)
Fink, A., Voß, S.: Applications of modern heuristic search methods to pattern sequencing problems. Computers & Operations Research 26 (1999) 17-34
Downey, R., Fellows, M.: Parameterized Complexity. Springer-Verlag (1998)
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Cotta, C., Fernàndez, A.J. (2007). Memetic Algorithms in Planning, Scheduling, and Timetabling. In: Dahal, K.P., Tan, K.C., Cowling, P.I. (eds) Evolutionary Scheduling. Studies in Computational Intelligence, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48584-1_1
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