article

A comprehensive analysis of hyper-heuristics

Authors:

Emin Erkan KorkmazAuthors Info & Claims

Intelligent Data Analysis, Volume 12, Issue 1

Pages 3 - 23

Published: 01 January 2008 Publication History

Abstract

Meta-heuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a meta-heuristic for solving a problem in a certain domain. Hyper-heuristics introduce a novel approach for search and optimization. A hyper-heuristic method operates on top of a set of heuristics. The most appropriate heuristic is determined and applied automatically by the technique at each step to solve a given problem. Hyper-heuristics are therefore assumed to be problem independent and can be easily utilized by non-experts as well. In this study, a comprehensive analysis is carried out on hyper-heuristics. The best method is tested against genetic and memetic algorithms on fourteen benchmark functions. Additionally, new hyper-heuristic frameworks are evaluated for questioning the notion of problem independence.

References

[1]

D. Ackley, An Empirical Study of Bit Vector Function Optimization, In Proceedings of Genetic Algorithms and Simulated Annealing, 1987, 170-215.

[2]

M. Ayob and G. Kendall, A Monte Carlo Hyper-Heuristic to Optimise Component Placement Sequencing for Multi Head Placement Machine, In Proceedings of the Int. Conf. on Intelligent Technologies, 2003, 132-141.

[3]

B. Bilgin, E. Ozcan and E.E. Korkmaz, An Experimental Study on Hyper-Heuristics and Final Exam Scheduling, In Proceedings of the 2006 International Conference on the Practice and Theory of Automated Timetabling, 2006, 123-140.

[4]

E. Burke and E. Soubeiga, Scheduling Nurses Using a Tabu-Search Hyperheuristic, In Proceedings of the MISTA I, Nottingham, vol. 1, 2003, 197-218.

[5]

E.K. Burke, G. Kendall, J. Newall, E. Hart, P. Ross and S. Schulenburg, Hyper-heuristics an Emerging Direction in Modern Search Technology, in: Handbook of Metaheuristics, F. Glover and G.A. Kochenberger, eds, 2003, pp. 457-474.

[6]

E.K. Burke, G. Kendall and E. Soubeiga, A Tabu-Search Hyper-heuristic for Timetabling and Rostering, Journal of Heuristics 9(6) (2003), 451-470.

[7]

E.K. Burke, A. Meisels, S. Petrovic and R. Qu, A Graph-Based Hyper Heuristic for Timetabling Problems, European Journal of Operational Research 176 (2007), 177-192.

[8]

E.K. Burke S. Petrovic and R. Qu, Case Based Heuristic Selection for Timetabling Problems, Journal of Scheduling 9(2) (2006), 1094-6136.

[9]

H.G. Cobb, An investigation into the use of hypermutation as an adaptive operator in Genetic Algorithms Having Continuous, Time-dependent Nonstationary Environment, NRL Memorandum Report 6760, 1990.

[10]

P. Cowling and K. Chakhlevitch, Hyperheuristics for managing a large collection of low level heuristics to schedule personnel, In Proceedings of the Congress on Evolutionary Computation, vol. 2, 2003, 1214-1221.

[11]

P. Cowling, G. Kendall and E. Soubeiga, A Hyper-heuristic Approach to Scheduling a Sales Summit, LNCS 2079, PATAT III, Konstanz, Germany, selected papers (E.K. Burke and W. Erben, eds), 2000, 176-190.

[12]

A. Cuesta-Cañada, L. Garrido and H. Terashima-Marín, Building Hyper-heuristics Through Ant Colony Optimization for the 2D Bin Packing Problem, LNCS 3684, 2005, 654-660.

[13]

L. Davis, Bit Climbing, Representational Bias, and Test Suite Design, In Proceedings of the 4th Int. Conference on Genetic Algorithms, 1991, 18-23.

[14]

R. Dawkins, The Selfish Genes, Oxford University Press, 1976.

[15]

K. De Jong, An Analysis of the Behaviour of a Class of Genetic Adaptive Systems, PhD thesis, University of Michigan, 1975.

[16]

E.E. Easom, A Survey of Global Optimization Techniques, M. Eng. thesis, Univ. Louisville, Louisville, KY, 1990.

[17]

A. Gaw, P. Rattadilok and R.S.K. Kwan, Distributed Choice Function Hyperheuristics for Timetabling and Scheduling, In Proceedings of the 5th International Conference on the Practice and Theory of Automated Timetabling, 2004, 495-498.

[18]

D.E. Goldberg, Genetic Algorithms and Walsh Functions Part I, A Gentle Introduction, Complex Systems (1989), 129- 152.

[19]

D.E. Goldberg, Genetic Algorithms and Walsh Functions Part II, Deception and Its Analysis, Complex Systems (1989), 153-171.

[20]

D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading (MA), 1989.

[21]

A.O. Griewangk, Generalized Descent of Global Optimization, Journal of Optimization Theory and Applications 34 (1981), 11-39.

[22]

E. Hart, P. Ross and J. Nelson, Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder, Evolutionary Computation 6(1) (1998), 61-80.

[23]

J.H. Holland, Adaptation in Natural and Artificial Systems, Univ. Mich. Press, 1975.

[24]

G. Kendall and M. Mohamad, Channel Assignment in Cellular Communication Using a Great Deluge Hyper-heuristic, In Proceedings of the IEEE International Conference on Network, 2004, 769-773.

[25]

N. Krasnogor and S. Gustafson, A Study on the use of "Self-Generation" in Memetic Algorithms, Natural Computing 3(1) (2004), 53-76.

Digital Library

[26]

N. Krasnogor, Studies on the Theory and Design Space of Memetic Algorithms, PhD Thesis, University of the West of England, Bristol, UK, 2002.

[27]

N. Krasnogor and J.E. Smith, Multimeme Algorithms for the Structure Prediction and Structure Comparison of Proteins, In Proceedings of the Bird of a Feather Workshops, GECCO, 2002, 42-44.

[28]

N. Krasnogor and J.E. Smith, Emergence of Profitable Search strategies Based on a Simple Inheritance Mechanism, In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO, 2001, 432-439.

[29]

N. Krasnogor and J.E. Smith, A Memetic AlgorithmWith Self-Adaptive Local Search: TSP as a case study, In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO, 2000, 987-994.

[30]

M. Mitchell and S. Forrest, Fitness Landscapes Royal Road Functions, in: Handbook of Evolutionary Computation, T. Baeck, D. Fogel and Z. Michalewiz, eds, Institute of Physics Publishing and Oxford University, 1997.

[31]

P. Moscato and M.G. Norman, A Memetic Approach for the Traveling Salesman Problem Implementation of a Computational Ecology for Combinatorial Optimization on Message-Passing Systems, Parallel Computing and Transputer Applications (1992), 177-186.

[32]

Z. Ning, Y.S. Ong, K.W. Wong and M.H. Lim, Choice of Memes In Memetic Algorithm, In Proceedings of the 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems, 2003.

[33]

Y.S. Ong and A.J. Keane, Meta-Lamarckian Learning in Memetic Algorithms, IEEE Trans Evolutionary Computation 8(2) (2004), 99-110.

Digital Library

[34]

E. Ozcan, An Empirical Investigation on Memes, Self-generation and Nurse Rostering, In Proceedings of the 6th Int. Conf. on PATAT 2006, 246-263.

[35]

E. Ozcan, Memetic Algorithms for Nurse Rostering, LNCS 3733, The 20th ISCIS, 2005, 482-492.

[36]

E. Ozcan, B. Bilgin and E.E. Korkmaz, Hill Climbers and Mutational heuristic, LNCS 4193, PPSN IX, 2006, 202-211.

[37]

E. Ozcan and C. Basaran, A Case Study of Memetic Algorithms for Constraint Optimization: Multidimensional 0-1 Knapsack Problem, CSE-2006-01, Technical Report, 2006.

[38]

E. Ozcan and E. Onbasioglu, Memetic Algorithms for Parallel Code Optimization, International Journal of Parallel Processing 35(1) (February 2007), 33-61.

Digital Library

[39]

L.A. Rastrigin, Extremal Control Systems, In Theoretical Foundations of Engineering Cybernetics Series, Moscow, Nauka, Russian, 1974.

[40]

P. Ross, J.G. Marin-Blazquez and E. Hart, Hyper-heuristics applied to class and exam timetabling problems, In Proceedings of the Congress on Evolutionary Computation, vol. 2, 2004, 1691-1698.

[41]

H.P. Schwefel, Numerical Optimization of Computer Models, John Wiley & Sons (1981), English translation of Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie, 1977.

[42]

J.E. Smith and T.C. Fogarty, Operator and parameter adaptation in genetic algorithms, Soft Computing 1(2) (1997), 81-87.

[43]

D. Tasoulis, N. Pavlidis, V. Plagianakos and M. Vrahatis, Parallel Differential Evolution, In Proceedings of the IEEE Congress on Evolutionary Computation, 2004, 2023-2029.

[44]

D. Whitley, Fundamental Principles of Deception in Genetic Search, in: Foundations of Genetic Algorithms, G.J.E. Rawlins, ed., Morgan Kaufmann, San Matco, CA, 1991.

[45]

D. Wolpert and W.G. MacReady, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation 1(1) (1997), 67-82.

Digital Library

Cited By

Kheiri AKeedwell EWagner M(2022)Selection hyper-heuristicsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533655(983-996)Online publication date: 9-Jul-2022
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Dong JRong HNeri FYang QZhu MZhang G(2020)An Adaptive Memetic P System to Solve the 0/1 Knapsack Problem2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185841(1-8)Online publication date: 19-Jul-2020
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Sánchez-Díaz XOrtiz-Bayliss JAmaya ICruz-Duarte JConant-Pablos STerashima-Marín H(2020)A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185671(1-8)Online publication date: 19-Jul-2020
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Index Terms

A comprehensive analysis of hyper-heuristics

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cover image Intelligent Data Analysis

Intelligent Data Analysis Volume 12, Issue 1

January 2008

139 pages

ISSN:1088-467X

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IOS Press

Netherlands

Publication History

Published: 01 January 2008

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Cited By

Kheiri AKeedwell EWagner M(2022)Selection hyper-heuristicsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533655(983-996)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533655
Dong JRong HNeri FYang QZhu MZhang G(2020)An Adaptive Memetic P System to Solve the 0/1 Knapsack Problem2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185841(1-8)Online publication date: 19-Jul-2020
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Sánchez-Díaz XOrtiz-Bayliss JAmaya ICruz-Duarte JConant-Pablos STerashima-Marín H(2020)A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185671(1-8)Online publication date: 19-Jul-2020
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Cecilia JCutillas-Lozano JGiménez DImbernón B(2018)Exploiting multilevel parallelism on a many-core system for the application of hyperheuristics to a molecular docking problemThe Journal of Supercomputing10.1007/s11227-017-1989-774:5(1803-1814)Online publication date: 1-May-2018
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Bian YLi ZGuo JZhao R(2018)Concrete hyperheuristic framework for test case prioritizationJournal of Software: Evolution and Process10.1002/smr.199230:11Online publication date: 14-Nov-2018
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Kheiri AKeedwell E(2017)A hidden markov model approach to the problem of heuristic selection in hyper-heuristics with a case study in high school timetabling problemsEvolutionary Computation10.1162/evco_a_0018625:3(473-501)Online publication date: 1-Sep-2017
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Tyasnurita RÖzcan EJohn R(2017)Learning heuristic selection using a Time Delay Neural Network for Open Vehicle Routing2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969477(1474-1481)Online publication date: 5-Jun-2017
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Drake JÖzcan EBurke E(2016)A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problemEvolutionary Computation10.1162/EVCO_a_0014524:1(113-141)Online publication date: 1-Mar-2016
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Asta SKarapetyan DKheiri AÖzcan EParkes A(2016)Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problemInformation Sciences: an International Journal10.1016/j.ins.2016.09.010373:C(476-498)Online publication date: 10-Dec-2016
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