Article

Comparing two models to generate hyper-heuristics for the 2d-regular bin-packing problem

Authors:

H. Terashima-Marin,

C. J. Farias Zarate,

M. Valenzuela-RendonAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 2182 - 2189

https://doi.org/10.1145/1276958.1277377

Published: 07 July 2007 Publication History

Abstract

The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents two Evolutionary-Computation-based Models to producehyper-heuristics that solve two-dimensional bin-packing problems. The first model uses an XCS-type Learning Classifier System which learns a solution procedure when solving individual problems. The second model is based on a GA that uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through alearning process which includes training and testing phases.Both approaches, when tested and compared using a large set ofbenchmark problems, perform better than the combinations ofsingle heuristics. The testbed is composed of problems used inother similar studies in the literature. Some additional instances of the testbed were randomly generated.

References

[1]

H. Terashima-Marín Tecnológico de Monterrey, Monterrey, N. L., Mexico

[2]

C. J. Farias-Zárate Tecnológico de Monterrey, Monterrey, N. L., Mexico

[3]

P. Ross Napier University, Edinburgh, United Kingdom

[4]

M. Valenzuela-Rendón Tecnolégico de Monterrey, Monterrey, N. L., Mexico

[5]

W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone. Genetic programming: An Introduction. Morgan Kaufmann Publishers, Inc, London, 1998.

Digital Library

[6]

J. E. Beasley. Beasley operations research library. Collection of problems for 2D packing and cutting, 2003.

[7]

J. O. Berkey and PY. Wang. Two-dimensional finite bin packing algorithms. Journal of Operational Research Society, 38:423--429, 1987.

[8]

E. Burke, E. Hart, G. Kendall, J. Newall, P. Ross, and S. Schulenburg. Hyper-heuristics: An emerging direction in modern research technolology. In Handbook of Metaheuristics, pages 457--474. Kluwer Academic Publishers, 2003.

[9]

C. H. Cheng, B. R. Fiering, and T. C. Chang. The cutting stock problem. a survey. International Journal of Production Economics, 36:291--305, 1994.

[10]

H. Dyckhoff. A topology of cutting and packing problems. European Journal of Operation Research, 44:145--159, 1990.

[11]

D. B. Fogel, L. A. Owens, and M. Walsh. Artificial Intelligence through Simulated Evolution. Wiley, New York, 1966.

Digital Library

[12]

M. Garey and D. Johnson. Computers and Intractability. W.H. Freeman and Company, New York, 1979.

Digital Library

[13]

D. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Adison Wesley, 1989.

Digital Library

[14]

D. Goldberg, B. Korb, and K. Deb. Messy genetic algorithms: Motivation, analysis and first results. Complex Systems, pages pp. 93--130, 1989.

[15]

B. L. Golden. Approaches to the cutting stock problem. AIIE Transactions, 8:256--274, 1976.

[16]

J. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975.

Digital Library

[17]

E. Hopper and B. C. Turton. An empirical study of meta-heuristics applied to 2D rectangular bin packing. Studia Informatica Universalis, pages 77--106, 2001.

[18]

S. Jakobs. On genetic algorithms for the packing of polygons. European Journal of Operations Research, 88:165--181, 1966.

[19]

L. V. Kantorovich. Mathematical methods of organizing and planning production. Management Science, 6:366--422, 1960.

Digital Library

[20]

D. Liu and H. Teng. An improved BL-algorithm for genetic algorithm of the orthogonal packing of rectangle. European Journal of Operations Research, 112:413--419, 1999.

[21]

S. Martello and D. Vigo. Exact solution of the two-dimensional finite bin packing problem. Dipartimento di Elettronica, Informatica e Sistematica, 1998.

[22]

I. Rechenberg. Evolutionstrategie: Optimierung technischer systeme nach prinzipien dier biolischen evolution. Frommann-Holzboog, Stuttgart, 1973.

[23]

P. Ross, S. Schulenburg, J. M. Blázquez, and E. Hart. Hyper-heuristics: learning to combine simple heuristics in bin-packing problems. Proceedings of GECCO 2002, pages 942--948, 2002.

Digital Library

[24]

H. P. Schwefel. Numerical Optimization of Computer Models. Wiley, Chinchester, UK, 1981.

Digital Library

[25]

H. Terashima-Marín, CJ. Farías-Zárate, P. Ross, and M. Valenzuela-Rendón. A GA-based method to produce generalized hyper-heuristics for the 2D-regular cutting stock problem. Proceedings of the Genetic and Evolutionary Computation Conference 2006, pages 591--598, 2006.

Digital Library

[26]

H. Terashima-Marín, E. J. Flores-Álvarez, and P. Ross. Hyper-heuristics and classifier systems for solving 2D-regular cutting stock problems. Proceedings of the Genetic and Evolutionary Computation Conference 2005, pages 637--643, 2005.

Digital Library

[27]

R. A. Wilson and F. C. Keil. The MIT Encyclopedia of the Cognitive Science. MIT Press, Cambridge, Massachussets, 1999.

[28]

S. W. Wilson. Generalization in the XCS classifier system. Evolutionary Computation}, 1998.

[29]

G. Wäscher, H. Haussner, and H. Schumann. An improved typology of cutting and packing problems. European Journal of Operation Research, 171, 2006.

Cited By

Yin PChuang KHwang G(2016)Developing a context-aware ubiquitous learning system based on a hyper-heuristic approach by taking real-world constraints into accountUniversal Access in the Information Society10.1007/s10209-014-0390-z15:3(315-328)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s10209-014-0390-z
Khan KQureshi KAbd-El-Barr M(2014)An efficient grid scheduling strategy for data parallel applicationsThe Journal of Supercomputing10.1007/s11227-014-1114-068:3(1487-1502)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1007/s11227-014-1114-0
Garrido PCastro C(2012)A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing ProblemsFundamenta Informaticae10.5555/2385096.2385099119:1(29-60)Online publication date: 1-Jan-2012
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Index Terms

Comparing two models to generate hyper-heuristics for the 2d-regular bin-packing problem
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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July 7 - 11, 2007

London, England

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Yin PChuang KHwang G(2016)Developing a context-aware ubiquitous learning system based on a hyper-heuristic approach by taking real-world constraints into accountUniversal Access in the Information Society10.1007/s10209-014-0390-z15:3(315-328)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s10209-014-0390-z
Khan KQureshi KAbd-El-Barr M(2014)An efficient grid scheduling strategy for data parallel applicationsThe Journal of Supercomputing10.1007/s11227-014-1114-068:3(1487-1502)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1007/s11227-014-1114-0
Garrido PCastro C(2012)A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing ProblemsFundamenta Informaticae10.5555/2385096.2385099119:1(29-60)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.5555/2385096.2385099
Özcan EMisir MOchoa GBurke E(2012)A Reinforcement LearningModeling, Analysis, and Applications in Metaheuristic Computing10.4018/978-1-4666-0270-0.ch003(34-55)Online publication date: 2012
https://doi.org/10.4018/978-1-4666-0270-0.ch003
Burke EHyde MKendall GWoodward J(2012)Automating the packing heuristic design process with genetic programmingEvolutionary Computation10.1162/EVCO_a_0004420:1(63-89)Online publication date: 1-Mar-2012
https://dl.acm.org/doi/10.1162/EVCO_a_00044
Burke EHyde MKendall G(2012)Grammatical Evolution of Local Search HeuristicsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2011.216040116:3(406-417)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1109/TEVC.2011.2160401
Özcan EMisir MOchoa GBurke E(2010)A Reinforcement Learning-Great-Deluge Hyper-Heuristic for Examination TimetablingInternational Journal of Applied Metaheuristic Computing10.4018/jamc.20101026031:1(39-59)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.4018/jamc.2010102603
Burke EHyde MKendall G(2010)Providing a memory mechanism to enhance the evolutionary design of heuristicsIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586388(1-8)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5586388
Burke ECurtois THyde MKendall GOchoa GPetrovic SVazquez-Rodriguez JGendreau M(2010)Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithmsIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586064(1-8)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5586064
Hussain Shah SQureshi KRasheed H(2010)Optimal job packing, a backfill scheduling optimization for a cluster of workstationsThe Journal of Supercomputing10.1007/s11227-009-0332-354:3(381-399)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.1007/s11227-009-0332-3
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