Hyper-heuristic Publications

Burke, E. K., M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and R. Qu (2010). Hyper-heuristics: A Survey of the State of the Art, School of Computer Science and Information Technology, University of Nottingham, Computer Science Technical Report No. NOTTCS-TR-SUB-0906241418-2747.
Burke, E. K., M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and J. Woodward (2009). A Classification of Hyper-heuristics Approaches, Handbook of Metaheuristics, International Series in Operations Research & Management Science, In M. Gendreau and J-Y Potvin (Eds.), Springer (in press).
Burke, E. K., M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan and J. R. Woodward (2009) Exploring Hyper-heuristic Methodologies with Genetic Programming, Computational Intelligence: Collaboration, Fusion and Emergence, In C. Mumford and L. Jain (eds.), Intelligent Systems Reference Library, Springer, pp. 177-201
Burke, E. K., Kendall, G., Newall, J., Hart, E., Ross P. and Schulenburg, S. (2003) Hyper-Heuristics: An Emerging Direction in Modern Search Technology, Chapter 16 in Handbook of Meta-Heuristics, (Eds. F. Glover and G. Kochenberger), Kluwer Academic Publishers, 457–474.
Ross, P. (2005) Hyper-heuristics, Chapter 17 in Search Methodologies: Introductory Tutorials in Optimization and Decision Support Methodologies (Eds. E.K.Burke and G.Kendall), Springer, 529–556.

2010

Journal papers (2010)

Burke E. K., Hyde M, Kendall G, Woodward J (2010) A genetic programming hyper-heuristic approach for evolving two dimensional strip packing heuristics. IEEE Transactions on Evolutionary Computation (to appear).
Cano-Belman J., and J. Bautista (2010) A scatter search based hyper-heuristic for sequencing a mixed-model assembly line. Journal of Heuristics (to appear).
Garrido P., Riff M. C. (2010) DVRP: A hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic. Journal of Heuristics (to appear).
Lokketangen A., Olsson R. (2010) Generating meta-heuristic optimization code using adate. Journal of Heuristics (to appear).
Maturana J., Lardeux F., Saubion F. (2010) Autonomous operator management for evolutionary algorithms. Journal of Heuristics (to appear).
Meignan D., Koukam A., Creput J. C. (2010) Coalition-based metaheuristic: A self-adaptive metaheuristic using reinforcement learning and mimetism. Journal of Heuristics (to appear).
Ouelhadj D., Petrovic S. (2010) A cooperative hyper-heuristic search framework. Journal of Heuristics (to appear).
Vazquez-Rodriguez J. A., Petrovic S. (2010) A new dispatching rule based genetic algorithm for the multi-objective job shop problem. Journal of Heuristics (to appear).

Conference proceedings (2010)

A. Berberoglu, A. Şima Uyar (2010) A Hyper-Heuristic Approach for the Unit Commitment Problem. Evolutionary Computation in Combinatorial Optimization, 9th European Conference, EvoCOP 2010. Lecture Notes in Computer Science xxx, Springer, xxx-xxx.
J. Dubois-Lacoste, M. López Ibáñez and T. Stützle (2010) Adaptive "Anytime" Two-Phase Local Search. Learning and Intelligent OptimizatioN ,LION 4, Lecture Notes in Computer Science xxx, Springer, xxx-xxx.
J. Garcia-Nieto, E. Alba (2010) Automatic Parameter Tuning with Metaheuristics of the AODV Routing Protocol for Vehicular Ad-Hoc Networks. Evolutionary Computation in Combinatorial Optimization, 9th European Conference, EvoCOP 2010. Lecture Notes in Computer Science xxx, Springer, xxx-xxx.
A. Keles, A. Şima Uyar, A. Yayimli (2010) Solving the Physical Impairment Aware Routing and Wavelength Assignment Problem in Optical WDM Networks Using a Tabu Search Based Hyper-Heuristic Approach. Evolutionary Computation in Combinatorial Optimization, 9th European Conference, EvoCOP 2010. Lecture Notes in Computer Science xxx, Springer, xxx-xxx.
S. Muelas, J.M. Peña, A. LaTorre (2010) Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions. Evolutionary Computation in Combinatorial Optimization, 9th European Conference, EvoCOP 2010. Lecture Notes in Computer Science xxx, Springer, xxx-xxx.
S. Pirkwieser, G. R. Raidl (2010) Multilevel Variable Neighborhood Search for Periodic Routing Problems. Evolutionary Computation in Combinatorial Optimization, 9th European Conference, EvoCOP 2010. Lecture Notes in Computer Science xxx, Springer, xxx-xxx.
S. K. Smit, A. E. Eiben (2010) Parameter Tuning of Evolutionary Algorithms: Generalist vs. Specialist. Evolutionary Computation in Combinatorial Optimization, 9th European Conference, EvoCOP 2010. Lecture Notes in Computer Science xxx, Springer, xxx-xxx.

2009

Journal papers (2009)

Aickelin U, Burke EK, Li J (2009) An evolutionary squeaky wheel optimization approach to personnel scheduling. IEEE Transactions on Evolutionary Computation, 13(2):433–443.
Asmuni, H., Burke, E.K., Garibaldi, J. M.,McCollum, B., and Parkes, A. J. (2009). An Investigation of Fuzzy Multiple Heuristic Orderings in the Construction of University Examination Timetables. Computers and Operations Research, 36(4), 981-1001.
Hutter F, Hoos H, Leyton-Brown K, StÄutzle T (2009) Paramils: An automatic algorithm configuration framework. Journal of Artificial Intelligence Research (JAIR) 36:267306.
Pillay, N., and Banzhaf, W. (2009). A Study of Heuristic Combinations for Hyper-Heuristic
Systems for the Uncapacitated Examination Timetabling Problem. European Journal of Operational Research (EJOR), 197(2), 482-491.
Qu, R, E. K. Burke and B. McCollum. (2009) Adaptive Automated Construction of Hybrid Heuristics for Exam Timetabling and Graph Colouring Problems. European Journal of Operational Research (EJOR), 198(2): 392-404.
Qu R., Burke, E. K. (2009) Hybridisations within a graph based hyper-heuristic framework for university timetabling problems. Journal of the Operational Research Society 60:1273-1285.

Conference proceedings (2009)

Allen, S., E. K. Burke, M. Hyde (2009) Evolving Reusable 3D Packing Heuristics with Genetic
Programming, Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2008, Montreal, Canada, ACM , 931–938.
Bader-El-Din MB, Poli R (2009) Grammar-based genetic programming for timetabling, Proceedings of the IEEE Congress on Evolutionary Computation (CEC'09), Trondheim, Norway, pp 2532–2539.
Biazzini M, Banhelyi B, Montresor A, Jelasity M (2009) Distributed hyper-heuristics for real parameter optimization. Proceedings of the 11th Annual conference on Genetic and evolutionary computation GECCO '09, ACM, New York, NY, USA, pp 1339-1346.
Garrido P, Castro C (2009) Stable solving of cvrps using hyperheuristics. In: Genetic and Evolutionary Computation Conference (GECCO'09), ACM, Montreal, Canada, pp 255-262
Kumar R, Kumar Bal B, Rockett PI (2009) Multiobjective genetic programming approach to evolving heuristics for the bounded diameter minimum spanning tree problem. In: Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO '09), Montreal, Canada, pp 309-316
Maturana J, Fialho A, Saubion F, Schoenauer M, Sebag M (2009) Extreme compass and dynamic multi-armed bandits for adaptive operator selection. In: Proc. IEEE Congress on Evolutionary Computation CEC '09, pp 365-372.
Obit, J.H., D. Landa-Silva, D. Ouelhadj, M. Sevaux (2009) Non-Linear Great Deluge with Learning Mechanism for Solving the Course Timetabling Problem. Proceedings of the 8th Metaheuristics International Conference (MIC 2009).
Ochoa, G., R. Qu, E. K. Burke (2009) Analyzing the Landscape of a Graph Based Hyper-heuristic for Timetabling Problems. Proceedings of Genetic and Evolutionary Computation Conference (GECCO-09), ACM, pp. 341-348.
Ochoa, G., J. A. Vazquez-Rodriguez, S. Petrovic, E. K. Burke (2009) Dispatching Rules for Production Scheduling: A Hyper-heuristic Landscape Analysis. Proceedings of the IEEE Congress on Evolutionary Computation (CEC-09), IEEE Press, Trondheim, Norway.
Ozcan E, Bykov Y, Birben M, Burke EK (2009) Examination timetabling using late acceptance hyper-heuristics. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2009), pp 997-1004
Pillay N (2009) Evolving hyper-heuristics for the uncapacitated examination timetabling problem. In: Proceedings of the 4th Multidisciplinary International Con ference on Scheduling: Theory and Applications (MISTA'09), Dublin, Ireland, pp 447-457.
Vella A, D. Corne, C. Murphy (2009) Hyper-heuristic Decision Tree Induction, in World Congress on Nature and Biologically Inspired Computing (NABIC 2009), IEEE Press, to appear

2008

Journal papers (2008)

Bai R., Burke E.K. and Kendall G. (2008) Heuristic, Meta-heuristic and Hyper-heuristic Approaches for Fresh Produce Inventory Control and Shelf Space Allocation. Journal of the Operational Research Society, 59(10), pages 187-1397.
Fukunaga, A. S. (2008) Automated Discovery of Local Search Heuristics for Satisfiability Testing, Evolutionary Computation, Vol 16, No. 1, 21-61.
Ozcan E, Bilgin B, Korkmaz E.E. (2008) A comprehensive analysis of hyper-heuristics. Intelligent Data Analysis, 12:1, 3-23.
Smith-Miles KA (2008) Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys 41:6:1 - 6:25
Tay J.C and N. B. Ho, (2008) Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems, Computers & Industrial Engineering, vol. 54 (3), pp. 453-473.
Terashima-Marin H, Ross P, Farias-Zarate CJ, Lopez-Camacho E, Valenzuela-Rendon M (2008) Generalized hyper-heuristics for solving regular and irregular bin packing problems. Annals of Operations Research 1(1):1-10.
Thabtah F, Cowling P (2008) Mining the data from a hyperheuristic approach using associative classification. Expert Systems with Applications: An International Journal 34(2):1093-1101.

Conference proceedings (2008)

Bader-El-Din M. B. and R. Poli. (2008) An Incremental Approach for Improving Local Search Heuristics. Evolutionary Computation in Combinatorial Optimization, 8th European Conference, EvoCOP 2008, Naples, Italy, March 26-28, 2008. Lecture Notes in Computer Science 4972, Springer, 194-205.
Fialho A, Costa L, Schoenauer M, Sebag M (2008) Extreme value based adaptive operator selection. In: Proceedings of the 10th international conference on Parallel Problem Solving from Nature, Springer-Verlag, Berlin, Heidelberg, pp 175-184.
Kumar, R, A. H. Joshi, K. K. Banka, P. Rockett (2008) Evolution of hyperheuristics for the biobjective 0/1 knapsack problem by multiobjective genetic programming, Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2008, Atlanta, USA, ACM , 1227-1234.
Maturana J, Saubion F (2008) A compass to guide genetic algorithms. In: Proceed ings of the 10th international conference on Parallel Problem Solving from Nature, Springer-Verlag, Berlin, Heidelberg, pp 256-265.
Neri F, Tirronen V, KÄarkkÄainen T, Rossi T (2007) Fitness diversity based adaptation in multimeme algorithms: A comparative study. In: IEEE Congress on Evolutionary Computation, IEEE, pp 2374-2381
Pillay, N. (2008) An analysis of representations for hyper-heuristics for the uncapacitated examination timetabling problem in a genetic programming system.PProceedings of the 2008 Annual Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries, SAICSIT Conf. 2008, ACM International Conference Proceeding Series 338 , ACM, 188-192.
Smith-Miles K (2008) Towards insightful algorithm selection for optimisation using meta-learning concepts. In: Proc. IEEE World Congress on Computational Intelligence. IEEE International Joint Conference on Neural Networks IJCNN 2008, pp 4118-4124
Terashima-Marín, H, J. C. Ortiz-Bayliss, P. Ross, M. Valenzuela-Rendón (2008) Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems. Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2008, Atlanta, USA, ACM , 571-578.

2007

Journal papers (2007)

Burke, E.K., McCollum, B., Meisels, A., Petrovic, S. and Qu, R. (2007) A Graph-Based Hyper Heuristic for Educational Timetabling Problems, European Journal of Operational Research, Vol 176 issue 1, 177–192.
Dowsland, K., Soubeiga, E. and Burke, E.K. (2007), A Simulated Annealing Hyper-heuristic for Determining Shipper Sizes, European Journal of Operational Research, Volume 179, Issue 3, 16 June 2007, 759–774.
Ho, N. B., J. C. Tay and M-K. Lai (2007) An Effective Architecture for Learning and Evolving Flexible Job-Shop Schedules, European Journal of Operations Research, vol. 179 (2), pp. 316-333.
Neri, F., Toivanen J., Cascella G. L. , Ong YS (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biology Bioinform 4(2):264-278.
Pisinger, D. amd S. Ropke (2007) A general heuristic for vehicle routing problems, Computers & Operations Research, Vol. 34, Issue 8, 2403–2435.
Smith, J. E. (2007) Co-evolving Memetic Algorithms: A review and progress report. IEEE Transactions in Systems, Man and Cybernetics, part B. Vol 37:1 pp 6-17.

Conference proceedings (2007)

Asmuni, H., Burke, E.K., Garibaldi, J. M., and McCollum, B. (2007). Determining Rules in Fuzzy Multiple Heuristic Orderings for Constructing Examination Timetables. In proceedings of the 3rd Multidisciplinary International Scheduling: Theory and Applications Conference (MISTA 2007) (pp. 59-66), Springer.
Bader-El-Din M. B. and R. Poli. (2007) Generating SAT local-search heuristics using a GP hyper-heuristic framework. Proceedings of the 8th International Conference on Artifcial Evolution, 37-49.
Bai, R., E. K. Burke, G. Kendall and B. McCollum. (2007) Memory Length in Hyper-heuristics: An Empirical Study, Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling (CISched2007).
Bilgin, B., E. Özcan, E. E. Korkmaz. (2007) An Experimental Study on Hyper-Heuristics and Exam Scheduling, PATAT2006 selected papers, Springer-Verlag, LNCS.
Burke, E. K., M. Hyde, G.Kendall and J. Woodward. (2007) Automatic Heuristic Generation with Genetic Programming: Evolving a Jack-of-all-Trades or a master of one, Proceedings of the 2007 Genetic and Evolutionary Computation Conference (GECCO 2007), London, pp. 1559 - 1565.
Chen , P.C, Kendall G, Vanden-Berghe G (2007) An ant based hyper-heuristic for thetravelling tournament problem. In: Proceedings of IEEE Symposium of Computa tional Intelligence in Scheduling (CISched 2007), Hawaii, pp 19-26
Ersoy, E., Özcan, E., Uyar, S. (2007) Memetic Algorithms and Hyperhill-climbers, Proceedings of the 3rd Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2007).
Garrido, P. and M. C. Riff. (2007) An Evolutionary Hyperheuristic to Solve Strip-Packing Problems. Proceedings of Intelligent Data Engineering and Automated Learning - IDEAL 2007, 8th International Conference, Lecture Notes in Computer Science 4881, Springer, 406-415.
Garrido, P. and M. C. Riff. (2007) Collaboration Between Hyperheuristics to Solve Strip-Packing Problems. Proceedings of Foundations of Fuzzy Logic and Soft Computing, 12th International Fuzzy Systems Association World Congress, IFSA 2007, Lecture Notes in Computer Science 4529, Springer, 698-707.
Hutter F, Hoos HH, StÄutzle T (2007) Automatic algorithm configuration based on local search. In: AAAI, AAAI Press, pp 1152-1157
Jakobovic, D.; Jelenkovic, L.; Budin, L.; Genetic Programming Heuristics for Multiple Machine Scheduling. Genetic Programming, 10th European Conference, EuroGP 2007, Valencia, Spain, Lecture Notes in Computer Science, 4445, 321-330, 2007.
Keller, R. E. and R. Poli. (2007) Cost-Benefit Investigation of a Genetic-Programming Hyperheuristic. Proceedings of the 8th International Conference on Artifcial Evolution, 13-24.
Özcan, E., Alkan, A. (2007) A Memetic Algorithm for Solving a Timetabling Problem: An Incremental Strategy, Proceedings of the 3rd Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2007).
Özcan, E. (2007) An Empirical Investigation on Memes, Self-generation and Nurse Rostering, PATAT2006 selected papers, Springer-Verlag, LNCS.
Pillay, N. and W. Banzhaf (2007) A Genetic Programming Approach to the Generation of Hyper-Heuristics for the Uncapacitated Examination Timetabling Problem. Progress in Artificial Intelligence, 13th Portuguese Conference on Aritficial Intelligence, EPIA Workshops 2007, Lecture Notes in Computer Science 4874, Springer, 223-234.
Poli, R., J. Woodward and E.K. Burke (2007), A Histogram-matching Approach to the Evolution of Bin-packing Strategies, Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, 2007
Remde, S., P. I. Cowling, K. P. Dahal, N. Colledge. (2007) Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling. Evolutionary Computation in Combinatorial Optimization, 7th European Conference, EvoCOP 2007, Lecture Notes in Computer Science 4446, Springer, pp 188-197.
Terashima-Marín, H., C.J.F Zárate, P. Ross, M. Valenzuela-Rendón. (2007) Comparing Two Models to Generate Hyper-heuristics for the 2D-Regular Bin-Packing Problem. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007), pp 2182-2189.
Woodward, M. Hyde, E.K. Burke and G. Kendall (2007), Scalability of Evolved On Line Bin Packing Heuristics. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007).

2006

Journal papers (2006)

Burke, E.K., Petrovic, S. and Qu, R. (2006) Case Based Heuristic Selection for Timetabling Problems, Journal of Scheduling, Vol 9 issue 2, 115–132.
Geiger, C. D, Uzsoy, R. and Aytuğ, H.(2006) Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach, Journal of Scheduling , 9(1), Springer Netherlands , 7–34.
Ong, Y. S., M. H. Lim, N. Zhu and K. W. Wong (2006) Classification of Adaptive Memetic Algorithms: A Comparative Study, IEEE Transactions On Systems, Man and Cybernetics - Part B, Vol. 36, No. 1, pp. 141-152.

Conference proceedings (2006)

Burke, E.K, M. Hyde and G.Kendall. (2006) Evolving Bin Packing Heuristics with Genetic Programming, Proceedings of the 9th international conference on Parallel Problem Solving from Nature, Reykjavik, Iceland, 9-13 September 2006, Springer, Lecture Notes in Computer Science Volume 4193, pp 860-869.
JakobW(2006) Towards an adaptive multimeme algorithm for parameter optimisation suiting the engineers' needs. In: Parallel Problem Solving from Nature - PPSN IX, 9th International Conference, Springer, Lecture Notes in Computer Science, vol 4193, pp 132-141
Özcan, E., B. Bilgin, E. E. Korkmaz. (2006) Hill Climbers and Mutational Heuristics in Hyperheuristics, Lecture Notes in Computer Science, Springer-Verlag, The 9th International Conference on Parallel Problem Solving From Nature, pp. 202-211.
Terashima-Marín, H., Cláudia J. Farías Zárate, Peter Ross, Manuel Valenzuela-Rendón. (2006) A GA-based method to produce generalized hyper-heuristics for the 2D-regular cutting stock problem. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp 591-598.
Terashima-Marín, H., Cláudia J. Farías Zárate, Peter Ross, Manuel Valenzuela-Rendón. (2006) Two-Phase GA-Based Model to Learn Generalized Hyper-heuristics for the 2D-Cutting Stock Problem. IBERAMIA-SBIA 2006, Lecture Notes in Computer Science 4140, pp. 198-207.

2005

Journal papers (2005)

Krasnogor, N. and J.E. Smith. (2005) A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Transactions on Evolutionary Computation, 9(5):474- 488.
Oltean, M. (2005) Evolving Evolutionary Algorithms using Linear Genetic Programming, Evolutionary Computation, MIT Press, Vol. 13. Issue 3, pp 387-410.

Conference proceedings (2005)

Bai, R. and Kendall, G. (2005) An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-heuristics, In Meta-heuristics: Progress as Real Problem Solvers, Selected Papers from the 5th Metaheuristics International Conference (MIC 2003) (Eds. Ibaraki, T., Nonobe, K. & Yagiura, M.), pp. 87-108.
Burke,E.K., Dror, M., Petrovic, S. and Qu, R. (2005) Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems, B.L. Golden, S. Raghavan and E.A. Wasil (eds.). The Next Wave in Computing, Optimization, and Decision Technologies. Conference Volume of the 9th informs Computing Society Conference. Springer, pp. 79-91.
Burke, E.K., Kendall, G., Silva, J.D. Landa, O'Brien, R. & Soubeiga, E. (2005) An Ant Algorithm Hyperheuristic for the Project Presentation Scheduling Problem, in Proceedings of the 2005 IEEE Congress on Evolutionary Computation, pp 2263-2270.
Burke E.K., Landa Silva J.D., Soubeiga E. (2005) Multi-objective Hyper-heuristic Approaches for Space Allocation and Timetabling, In: Ibaraki T., Nonobe K., Yagiura M. (eds.), Meta-heuristics: Progress as Real Problem Solvers, Selected Papers from the 5th Metaheuristics International Conference (MIC 2003), Springer, pp. 129-158.
Chakhlevitch, K. Peter I. Cowling. (2005) Choosing the Fittest Subset of Low Level Heuristics in a Hyperheuristic Framework. Evolutionary Computation in Combinatorial Optimization, 5th European Conference, EvoCOP 2005, Lecture Notes in Computer Science 3448, Springer, pp. 23-33.
Cicirello, V.A., S.F. Smith. (2004) The Max K-Armed Bandit: A New Model of Exploration Applied to Search Heuristic Selection. In Proceedings of the Twentieth National Conference on Artificial Intelligence AAAI 2005: 1355-1361.
Cuesta-Cañada,A., Leonardo Garrido, Hugo Terashima-Marín (2005) Building Hyper-heuristics Through Ant Colony Optimization for the 2D Bin Packing Problem. Proceedings of Knowledge-Based Intelligent Information and Engineering Systems (KES 2005), Lecture Notes in Computer Science 3684, Springer, pp. 654-660.
Ho, N.B. and Tay, J.C. (2005) Evolving Dispatching Rules for solving the Flexible Job-Shop Problem, In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 05).
Kendall G. and Mohd Hussin N. (2005) Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology, Practice and Theory of Automated Timetabling V (eds E.K.Burke and M.Trick), Springer, Lecture notes in Computer Science, Vol 3616, pp 270-293.
Kendall G. and Mohd Hussin N. (2005) An Investigation of a Tabu Search Based Hyper-heuristic for Examination Timetabling, Multi-disciplinary Scheduling: Theory and Applications (eds G.Kendall, E.K.Burke, S.Petrovic and M.Gendreau) Selected papers from the 1st International Conference on Multi-disciplinary Scheduling: Theory and Applications, Nottingham, Springer, pp 309-328.
Rattadilok, P., Gaw, A. and Kwan, R.S.K. (2005) Distributed Choice Function Hyper-heuristics for Timetabling and Scheduling, Practice and Theory of Automated Timetabling V (eds E.K.Burke and M.Trick), Springer, Lecture notes in Computer Science Vol 3616, 2005, pp 51-67.
Terashima-Marín, H., E. J. Flores-Álvarez, Peter Ross. (2005) Hyper-heuristics and classifier systems for solving 2D-regular cutting stock problems. Proceeedings of the Genetic and Evolutionary Computation Conference (GECCO 2005), ACM, pp. 637-643.
Qu, R. and Burke E. K. (2005) Hybrid Variable Neighbourhood Hyper-heuristics for Exam Timetabling Problems, in Proceedings of the 6th Metaheuristic International Conference (MIC 2005).

2004

Journal papers (2004)

Burke, E. K. and Newall, J.P. (2004) Solving Examination Timetabling Problems through Adaptation of HeuristicOrderings, Annals of Operations Research 129, 107–134.
Krasnogor, N.(2004) Self-generating metaheuristics in bioinformatics: The protein structure comparison case,Genetic Programming and Evolvable Machines. Kluwer academic Publishers, vol. 5, no. 2, pp. 181–201.
Krasnogor, N. and S. Gustafson. (2004) A study on the use of selfgeneration in memetic algorithms, Natural Computing, vol. 3, no. 1,
pp. 53–76, 2004.
Ong, Y. S. and A.J. Keane (2004) Meta-Lamarckian Learning in Memetic Algorithm, IEEE Transactions On Evolutionary Computation, Vol. 8, No. 2, pp. 99-110.

Conference proceedings (2004)

Ayob, M. and Kendall, G. (2003) A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine, Proceedings of the International Conference on Intelligent Technologies, InTech'03, pp 132-141, Chiang Mai, Thailand, Dec 17-19.
Cicirello, V. A., and S. F. Smith. (2004) Heuristic Selection for Stochastic Search Optimization: Modeling Solution Quality by Extreme Value Theory. In Principles and Practice of Constraint Programming CP 2004: 197-211
Gaw A., Rattadilok P., Kwan R.S.K. (2004). Distributed Choice Function Hyper-Heuristics for Timetabling and Scheduling. International Conference on the Practice and Theory of Automated Timetabling (PATAT 2004), pp. 495-497.
Kendall, G. and Hussin, N. Mohd (2004) An Investigation of a Tabu Search Based Hyper-heuristic for Examination Timetabling, in Selected papers from MISTA 2003 (Eds. Kendall, G., Burke, E.K. & Petrovic, S.).
Kendall, G. & Mohamad, M. (2004) Channel Assignment In Cellular Communication Using A Great Deluge Hyper-heuristic, in IEEE International Conference on Network (ICON2004), pp 769-773
Pappa, G.L., Freitas, A.A. (2004) Towards a genetic programming algorithm for automatically evolving rule induction algorithms. In Furnkranz, J., ed.: Proc.ECML/PKDD-2004 Workshop on Advances
in Inductive Learning. (2004) 93—08.

Ross, P., Marin-Blazquez J. and Hart E. (2004) Hyper-heuristics applied to class and exam timetabling problems. Proceedings of the 2004 congress on evolutionary computation (CEC 2004).

2003

Journal papers (2003)

Burke EK, Kendall G, Soubeiga E (2003b) A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics 9(6):451-470.

Conference proceedings (2003)

Ayob, M. & Kendall, G. (2003) A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine, in Proceedings of the International Conference on Intelligent Technologies (InTech'03), pp 132-141.
Burke, E. K., MacCarthy, B., Petrovic, S., Qu, R (2002). Knowledge Discovery in a Hyper-Heuristic Using Case-Based Reasoning on Course Timetabling. Proceedings of the 4th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2002), Springer, Lecture Notes in Computer Science Volume 2740, pp. 276-86.
Cowling, P. and K. Chakhlevitch (2003) Hyperheuristics for managing a large collection of low level heuristics to schedule personnel, in Proceedings of the 2003 IEEE Congress on Evolutionary Computation (CEC’2003), vol. 2, pp 1214 – 1221.
Han, L., Kendall, G., (2003) Investigation of a Tabu Assisted Hyper-Heuristic Genetic Algorithm, Proceedings of Congress on Evolutionary Computation (CEC 2003), Vol 3, Canberra, Australia, Dec 8-12, pp 2230-2237.
Han L. and Kendall G. (2003) Guided Operators for a Hyper-Heuristic Genetic Algorithm. In proceedings of AI-2003: Advances in Artificial Intelligence. The 16th Australian Conference on Artificial Intelligence, Springer, Lecture Notes in Artificial Intelligence, Vol 2903, pp 807-820.
Nareyek, A., S.F. Smith and C. M. Ohler (2003) Integrating Local-Search Advice into Refinement Search (Or Not).In Proceedings of the CP 2003 Third International Workshop on Cooperative Solvers in Constraint Programming, 29-43.
Ross, P. Javier G. Marín-Blázquez, Sonia Schulenburg, Emma Hart. (2003) Learning a Procedure That Can Solve Hard Bin-Packing Problems: A New GA-Based Approach to Hyper-heuristics, Proceeedings of the Genetic and Evolutionary Computation Conference (GECCO 2003), Springer Lecture Notes in Computer Science vol 2724, pp 1295-1306.
Stephenson M, OReilly U, Martin M, Amarasinghe S (2003) Meta optimization: Improving compiler heuristics with machine learning. In: Proceedings of the Conference on Programming Language Design and Implementation (SIGPLAN03), San Diego, CA, USA, pp 77-90
Thabtah, F., Cowling P., and Peng Y. (2003) Data Mining in Hyperheuristic framework. Proceedings of the Fifth Informatics Workshop,pp 117-120.
Smith, J. (2003) Co-evolving memetic algorithms: A learning approach to robust scalable optimisation, in Proceedings of IEEE Congress on Evolutionary Computation: CEC-03, 2003, pp. 498–505.

2002

Journal papers (2002)

Aler, R. and Borrajo, D. and Isasi, P. (2002) Using genetic programming to learn and improve control knowledge, Artificial Intelligence, Volume = 141, Issues 1-2, 2956.
Nguyen X. L, Subbarao Kambhampati and Romeo S. Nigenda (2002) Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search, Artificial Intelligence, Volume 135, Issues 1-2, 73–123.
Schmiedle, F., N. Drechsler, D. Große and R. Drechsler, (2002) Heuristic Learning based on Genetic Programming, Genetic Programming and Evolvable Machines, Volume 3, pp. 363-388.
Spector, L. and Robinson, A., (2002) Genetic Programming and Autoconstructive Evolution with the Push Programming Language,Genetic Programming and Evolvable Machines, Nr. 1, , Kluwer, pages 7-40.

Conference proceedings (2002)

Burke, E.K., MacCarthy, B., Petrovic, S. & Qu, R.(2002) Knowledge Discovery in Hyper-Heuristic Using Case-Based Reasoning on Course Timetabling, in Proceedings of the 4th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2002), pp 276-286.
Cowling P., Kendall G. and Han L.(2002) An Investigation of a Hyperheuristic Genetic Algorithm Applied to a Trainer Scheduling Problem. In proceedings of Congress on Evolutionary Computation (CEC2002), pp 1185-1190.
Cowling, P., Kendall, G. and Han, L. (2002) An Adaptive Length Chromosome Hyperheuristic Genetic Algorithm for a Trainer Scheduling Problem, in Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02), pp 267-271.
Cowling, P., Kendall, G. & Soubeiga, E. (2002) Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation, in Applications of Evolutionary Computing: Proceeding of Evo Workshops 2002 (Eds. Cagoni, S., Gottlieb, J., Hart, E., Middendorf, M. & Goenther, R.), pp 1-10.
Cowling, P., Kendall, G. & Soubeiga, E. (2002) Choice Function and Random HyperHeuristics, in Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 2002) , pp 667-671.
Fukunaga A. S. (2002) Automated discovery of composite sat variable-selection heuristics. In Eighteenth national conference on Artificial intelligence, pages 641-648, Menlo Park, CA, USA, American Association for Artificial Intelligence.
Jakob W (2002) HyGLEAM - an approach to generally applicable hybridization of evolutionary algorithms. In: Parallel Problem Solving from Nature - PPSN VII, Springer, Berlin, pp 527-536.
Petrovic, S., Qu, R. (2002) Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems. Proceedings of the 6th International Conference on Knowledge-Based Intelligent Information Engineering Systems and Applied Technologies (KES'02), Vol. 82, Milan, Italy, pp. 336-40.
Ross, P., Schulenburg, S., J.G.Marín-Blázquez and E.Hart. (2002) Hyper-heuristics: learning to combine simple heuristics in bin-packing problems, Proceddings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp 942-948.
Schulenburg, S. and Ross, P. and Marin-Blazquez and Emma Hart. (2002) A Hyper-Heuristic Aproach to Single and Multi-Step Environments in Bin-Packing Problems. 5th International Workshop on Learning Classifier Systems, IWLCS.
Smith, J. (2002) Co-evolving memetic algorithms: inital investigations. In Parallel Problem Solving from Nature VII, PPSN 2002, Lecture Notes in Computer Science, Springer-Verlag, 537–546.

2001

Journal papers (2001)

Dimopoulos, C., & Zalzala, A. M. S. (2001) Investigating the use of genetic programming for a classic one-machine scheduling problem.Advances in Engineering Software, 32(6), 489–498, 2001.
Edmonds, B., (2001) Meta-Genetic Programming: Co-evolving the Operators of Variation. Electrik on AI, Vol. 9, pages 13-29.

Conference proceedings (2001)

Cowling P., Kendall G. and Soubeiga E. (2001) A Parameter-Free Hyperheuristic for Scheduling a Sales Summit. In Proceedings of 4th Metahuristics International Conference (MIC 2001), pp 127-131.
Krasnogor, N. and J. Smith. (2001) Emergence of profitable search strategies based on a simple inheritance mechanism. In E. Goodman A. Wu-W.B. Langdon H.-M. Voigt-M. Gen-S. Sen M. Dorigo S. Pezeshk M. Garzon Spector, L. and E. Burke, editors, International Genetic and Evolutionary Computation Conference (GECCO2001), pages 432-439, San Francisco, CA, Morgan Kaufmann Publishers.

2000

Journal papers (2000)

Conference proceedings (2000)

Cowling P., Kendall G., Soubeiga E. (2000), A Hyperheuristic Approach to Scheduling a Sales Summit. In Practice and Theory of Automated Timetabling III : Third International Conference, PATAT 2000, Konstanz, Germany, August 2000, selected papers (eds Burke E.K. and Erben W), Springer, Lecture Notes in Computer Science Vol 2079, pp 176-190.
Krasnogor, N. and J. Smith. (2000) A memetic algorithm with self-adaptive local search: Tsp as a case study. In Cantu-Paz Spector Parmee Whitley, Goldberg and Beyer, editors, International Genetic and Evolutionary Computation Conference (GECCO2000), pages 987-994. Morgan Kaufmann.

Other book chapters

Araya, I., B. Neveu, M. C. Riff. (2008) An Efficient Hyperheuristic for Strip-Packing Problems. Adaptive and Multilevel Metaheuristics, Studies in Computational Intelligence, Vol. 136, (Eds. C. Cotta, M. Sevaux, K. Sörensen) Springer, pp. 61-76.
Burke E.K., Landa Silva J.D., Soubeiga E. (2005) Multi-objective Hyper-heuristic Approaches for Space Allocation and Timetabling, In: Ibaraki T., Nonobe K., Yagiura M. (eds.), Meta-heuristics: Progress as Real Problem Solvers, Selected Papers from the 5th Metaheuristics International Conference (MIC 2003), Springer, pp. 129-158.
Chakhlevitch, K, P. I. Cowling (2008) Hyperheuristics: Recent Developments. Adaptive and Multilevel Metaheuristics, Studies in Computational Intelligence, Vol. 136, (Eds. C. Cotta, M. Sevaux, K. Sörensen) Springer, pp. 3-29
Cowling, P.r I. K.n Chakhlevitch (2007) Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling. Evolutionary Scheduling, Studies in Computational Intelligence, Vol. 49, (Eds. K. P. Dahal, K. C. Tan, P.I. Cowling), Springer 543-576.
Nareyek, A. (2003). Choosing Search Heuristics by Non-Stationary Reinforcement Learning. In Metaheuristics: Computer Decision-Making, (Eds. Resende, M. G. C., and de Sousa, J. P.), Kluwer Academic Publishers, 523-544.
Vázquez-Rodríguez, J.A, and A. Salhi. (2007) A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow Shop Scheduling, in Evolutionary Scheduling (Eds K. Dahal, K. C. Tan and P. Cowling) , Springer, 125–142.
Smith, J. (2004) The co-evolution of memetic algorithms for protein structure prediction, in Recent Advances in Memetic Algorithms, W. Hart, N. Krasnogor, and J. Smith, Eds. Springer, pp. 105–128.
Wah BW, Ieumwananonthachai A (1999) Teacher: A genetics-based system for learning and for generalizing heuristics. In: Yao X (ed) Evolutionary Computation, World Scientific Publishing Co. Pte. Ltd., pp 124-170

Origins and early hyper-heuristic approaches

Fisher H. and Thompson G.L. (1961) Probabilistic Learning Combinations of Local Job-shop Scheduling Rules. In Factory Scheduling Conference, Carnegie Institute of Technology.
Crowston W.B., Glover F., Thompson G.L. and Trawick J.D. (1963) Probabilistic and Parameter Learning Combinations of Local Job Shop Scheduling Rules. ONR Research Memorandum, GSIA, Carnegie Mellon University, Pittsburgh, (117).
Smith, S.F. (1983) Flexible learning of problem solving heuristics through adaptive search. In Proceedings of the Eigth International Joint Conference on Artificial Intelligence, pages 422–425.
Grefenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics SMC-16(1):122-128.
Davis L (1989) Adapting operator probabilities in genetic algorithms. In: Schaffer JD (ed) Proceedings of the 3rd International Conference on Genetic Algorithms (ICGA89), San Francisco, pp 61-69
Mockus, J. and L. Mockus. (1991) Bayesian approach to global optimization and applications to multi-objective constrained problems, Journal of Optimization Theory and Application, Vol 70 No 1,155-171.
Storer, R.H., Wu, S.D and Vaccari, R (1992) New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling, Management Science, Vol 38 No 10, 1495-1509.
Fang H, Ross P, Corne D (1993) A promising genetic algorithm approach to job shopscheduling, rescheduling, and open-shop scheduling problems. In: Forrest S (ed) Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, pp 375-382.
Gratch J, Chien S, DeJong G (1993) Learning search control knowledge for deep space network scheduling. In: Proceedings of the Tenth International Conference on Machine Learning, Amherst, MA, pp 135-142
Fang H, Ross P, Corne D (1994) A promising hybrid ga/ heuristic approach for open-shop scheduling problems. In: Cohn A (ed) Eleventh European Conference on Artifcial Intelligence, John Wiley & Sons
Mockus, J. (1994) Application of Bayesian approach to numerical methods of global and stochastic optimization, Journal of Global Optimization, Vol 4 No. 4, 347-366.
Dorndorf, U. and E. Pesch. (1995) Evolution based learning in a job shop scheduling environment. Computers & Operations Research, 22, 25-40.
Storer, R. H., Wu S. D. and Vaccari, R. (1995). Problem and heuristic space search strategies for job shop scheduling. ORSA Journal of Computing, 7(4):453–467.
Wah BW, Ieumwananonthachai A, Chu LC, Aizawa A (1995) Genetics-based learning of new heuristics: Rational scheduling of experiments and generalization. IEEE Trans on Knowledge and Data Engineering 7(5):763-785.
Zhang, W. and Dietterich, T. G. (1995) A reinforcement learning approach to job-shop scheduling. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp 1114-1120.
Battiti R (1996) Reactive search: Toward self-tuning heuristics. In: Rayward-Smith VJ, Osman IH, Reeves CR, Smith GD (eds) Modern Heuristic Search Methods, John Wiley & Sons Ltd., Chichester, pp 61-83
Drechsler R, GÄockel N, Becker B (1996) Learning heuristics for obdd minimization by evolutionary algorithms. In: LNCS 1141. Proceedings of the 4th International Conference on Parallel Problem Solving from Nature (PPSN'96), Berlin, Germany, pp 730-739
Gratch, J. and DeJong, G. (1996) A Statistical Approach to Adaptive Problem Solving. Artif. Intell, Vol. 88, No 1-2, 101-142.
Gratch, J. and Chien, S. A. (1996) Adaptive Problem-solving for Large-scale Scheduling Problems: A Case Study. J. Artif. Intell. Res. (JAIR), Vol. 4, 365-396.
Minton S (1996) Automatically configuring constraint satisfaction problems: a case study. Constraints 1 (1):7-43.
Denzinger J, Fuchs M, Fuchs M (1997) High performance ATP systems by combining several ai methods. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 97), pp 102-107.
Lobo F, Goldberg D (1997) Decision making in a hybrid genetic algorithm. In: Proc. IEEE International Conference on Evolutionary Computation, pp 121-125.
Mladenovic N, Hansen P (1997) Variable neighborhood search. Computers and Operations Research 24(11):1097-1100.
Smith J, Fogarty T.C. (1997) Operator and parameter adaptation in genetic algorithms. Soft Comput 1(2) 81–87.
Hart E, Ross P (1998) A heuristic combination method for solving job-shop scheduling problems. In: Eiben AE, Back T, Schoenauer M, Schwefel HP (eds) Parallel Problem Solving from Nature V, Springer-Verlag, Lecture Notes in Computer Science, vol 1498, pp 845-854
Hart, E., Nelson, J. and Ross, P. (1998) Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder. Evolutionary Computation 6(1): 61-80.
Tuson A, Ross P (1998) Adapting operator settings in genetic algorithms. Evolutionary
Computation 6:161-184.
Joslin D, Clements DP (1999) "Squeaky Wheel" optimization. Journal of Artificial Intelligence Research 10:353-373.
Terashima-Marin H., Ross P. and Valenzuela-Rendón M. Evolution of Constraint Satisfaction Strategies in Examination Timetabling. In Proc. Genetic and Evolutionary Computation Conference (GECCO 1999). pp. 635-642.

Last Update: 08 March 2010