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Cognitive Computation
Erschienen in: Cognitive Computation 1/2014

01.03.2014

Searching the Hyper-heuristic Design Space

verfasst von: Jerry Swan, John Woodward, Ender Özcan, Graham Kendall, Edmund Burke

Erschienen in: Cognitive Computation | Ausgabe 1/2014

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Abstract

We extend a previous mathematical formulation of hyper-heuristics to reflect the emerging generalization of the concept. We show that this leads naturally to a recursive definition of hyper-heuristics and to a division of responsibility that is suggestive of a blackboard architecture, in which individual heuristics annotate a shared workspace with information that may also be exploited by other heuristics. Such a framework invites consideration of the kind of relaxations of the domain barrier that can be achieved without loss of generality. We give a concrete example of this architecture with an application to the 3-SAT domain that significantly improves on a related token-ring hyper-heuristic.
Vorheriger Artikel Taxonomical Associative Memory
Nächster Artikel Analogical Mapping with Sparse Distributed Memory: A Simple Model that Learns to Generalize from Examples

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Fußnoten
Literatur
1.
Bishop JM, Erden YJ. Computational creativity, intelligence and autonomy. Cognit Comput. 2012;4(3):209–1.CrossRef
2.
3.
d’Inverno M, Luck M. Creativity through autonomy and interaction. Cognit Comput. 2012;4(3):332–46.CrossRef
4.
Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Woodward JR. A classification of hyper-heuristics approaches. In: Gendreau M, Potvin J-Y, editors. Handbook of metaheuristics, 2nd Edition, vol 57 of international series in operations research & management science. Berlin:Springer; 2010. Ch. 15, p. 449–68.
5.
Hayes-Roth B. A blackboard architecture for control. Artif Intell. 1985;26(3):251–21.CrossRef
6.
Denzinger J, Fuchs M, Fuchs M. High performance ATP systems by combining several AI methods, Tech. rep., University of Kaiserslautern 1997.
7.
Burke EK, Kendall G, Soubeiga E . A tabu-search hyperheuristic for timetabling and rostering. J Heuristics. 2003;9(6):451–70.CrossRef
8.
Rattadilok P, Gaw A, Kwan RK. Distributed choice function hyper-heuristics for timetabling and scheduling. In: Burke E, Trick M, editors. Practice and theory of automated timetabling, vol. 3616 of Lecture Notes in Computer Science. Springer: Berlin; 2005. p. 51–67.
9.
Cowling P, Chekhlevitch K. Hyperheuristics for managing a large collection of low-level heuristics to schedule personnel. In: The 2003 congress on evolutionary computation (CEC ‘03), vol. 2; 2003. p. 1214–21.
10.
Woodward J, Parkes A, Ochoa G. A mathematical formalization of hyper-heuristics, Presented to the ’Workshop on Hyper-Heuristics’ at 10th international conference on parallel problem solving from nature (PPSN-08), Technische University Dortmund, Germany. (September 2008).
11.
12.
Battiti R, Tecchiolli G. The reactive tabu search. INFORMS J Comput 1994;6(2):126–40.CrossRef
13.
Spinellis D. Another level of indirection. In: Oram A, Wilson G editors. Beautiful code: leading programmers explain how they think. Sebastopol: O’Reilly and Associates; 2007. Ch. 17, p. 279–91.
14.
Swan J, Özcan E, Kendall G. Hyperion - a recursive hyper-heuristic framework. In: Coello C editors. Learning and intelligent optimization, Vol. 6683 of Lecture Notes in Computer Science. Springer: Berlin; 2011. p. 616–30.
15.
16.
Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: a survey. J Artif Intell Res (JAIR) 1996;4:237–85.
17.
18.
Eiben AE, Ruttkay Z. Self-adaptivity for constraint satisfaction: learning penalty functions In: International conference on evolutionary computation. 1996, p. 258–61.
19.
Marín-Blázquez JG, Schulenburg S. A hyper-heuristic framework with xcs: learning to create novel problem-solving algorithms constructed from simpler algorithmic ingredients. In: IWLCS, 2005, p. 193–218.
20.
Burke EK, Petrovic S, Qu R. Case-based heuristic selection for timetabling problems. J Sched. 2006;9(2):115–32.CrossRef
21.
Burke EK, Hyde MR, Kendall G, Ochoa G, Ozcan E, Woodward JR. Exploring hyper-heuristic methodologies with genetic programming. In: Mumford CL, Jain LC editors. Computational intelligence, Vol. 1 of intelligent systems reference library. Berlin:Springer; 2009. Ch. 6, p. 177–201.
22.
23.
Stadler PF. Towards a theory of landscapes. In: Lpez-Pena R, Capovilla R, Garca-Pelayo R, Waelbroeck H, Zertuche F editors. Complex systems and binary networks, Vol. 461 of Lecture notes in physics. Berlin: Springer; 1995. p. 77–163.
24.
25.
Reeves CR. Landscapes, operators and heuristic search. Ann Oper Res 1999;86:473–90.CrossRef
26.
Reeves CR. Fitness landscapes and evolutionary algorithms. In: AE ’99: selected papers from the 4th European conference on artificial evolution. London: Springer; 2000. p. 3–20.
27.
Kallel L, Naudts B, Reeves CR. Properties of fitness functions and search landscapes. London: Springer; 2001. p. 175–206.
28.
Weinberger E. Correlated and uncorrelated fitness landscapes and how to tell the difference. Biol Cybern 1990;63(5):325–36.CrossRef
29.
Berry DA, Fristedt B. Bandit problems: sequential allocation of experiments. Berlin: Springer; 1985.CrossRef
30.
31.
Milano M, Roli A. Magma: a multiagent architecture for metaheuristics, systems, man, and cybernetics, part B: cybernetics. IEEE Trans. 2004;34(2):925–941 doi:10.​1109/​TSMCB.​2003.​818432.
32.
33.
Brooks R. Intelligence without representation. Artif Intell 1991;47:139–59.CrossRef
34.
Burke E, Kendall G, Newall J, Hart E, Ross P, Schulenburg S. Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberger G, Hillier FS, editors. Handbook of Metaheuristics, Vol. 57 of international series in operations research and management science. New York: Springer; 2003. p. 457–74.CrossRef
35.
Booch G, Maksimchuk RA, Engle MW, Young BJ, Connallen J, Houston KA. Object-oriented analysis and design with applications, third edition, ACM SIGSOFT software engineering notes 33(5).
36.
Carver N, Lesser V. The evolution of blackboard control architectures, Tech. rep., Amherst, USA; 1992.
37.
al Rifaie MM, Bishop JM, Caines S. Creativity and autonomy in swarm intelligence systems. Cognit Comput 2012;4(3):320–31.CrossRef
38.
Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science 1983;220:671–80.CrossRefPubMed
39.
White S. Concepts of scale in simulated annealing. In: Proceedings of international conference on computer design; 1984, p. 646–51.
40.
Hoos HH, Stützle T. SATLIB: An online resource for research on SAT. In: Gent IP, Maaren Hv, Walsh T, editors. SAT 2000, SATLIB is available online at http://​www.​satlib.​org 2000.
41.
Xu L, Hutter F, Hoos HH, Leyton-Brown K. Satzilla: portfolio-based algorithm selection for sat. J Artif Int Res 2008;32(1):565–606.
42.
Gaspero LD, Schaerf A. Easylocal++: an object-oriented framework for the flexible design of local-search algorithms. Softw Pract Exper 2003;33(8):733–765.CrossRef
43.
Jones T, Forrest S. Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of the sixth international conference on genetic algorithms. Morgan Kaufmann; 1995. p. 184–92.
44.
Birattari M, Stützle T, Paquete L, Varrentrapp K. A racing algorithm for configuring metaheuristics. In: Proceedings of the genetic and evolutionary computation conference, GECCO ’02. San Francisco :Morgan Kaufmann Publishers Inc.; 2002. p. 11–18.
Metadaten
Titel
Searching the Hyper-heuristic Design Space
verfasst von
Jerry Swan
John Woodward
Ender Özcan
Graham Kendall
Edmund Burke
Publikationsdatum
01.03.2014
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 1/2014
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-013-9201-8

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