Article

Structure and metaheuristics

Authors:

Yossi Borenstein,

Riccardo PoliAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 1087 - 1094

https://doi.org/10.1145/1143997.1144172

Published: 08 July 2006 Publication History

Abstract

Metaheuristics have often been shown to be effective for difficult combinatorial optimization problems. The reason for that, however, remains unclear. A framework for a theory of metaheuristics crucially depends on a formal representative model of such algorithms. This paper unifies/reconciles in a single framework the model of a black box algorithm coming from the no-free-lunch research (e.g. Wolpert et al. [25], Wegener [23]) with the study of fitness landscape. Both are important to the understanding of meta-heuristics, but they have so far been studied separately. The new model is a natural environment to study meta-heuristics.

References

[1]

T. Blickle and L. Thiele. A comparison of selection schemes used in evolutionary algorithms. Evolutionary Computation, 4(4):361--394, 1996.

Digital Library

[2]

C. Blum and A. Roli. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv., 35(3):268--308, 2003.

Digital Library

[3]

Y. Borenstein and R. Poli. Structure and metaheuristics. GECCO 2006.

Digital Library

[4]

Y. Borenstein and R. Poli. Information landscapes. In GECCO '05: Proceedings of the 2005 conference on Genetic and evolutionary computation, pages 1515--1522, New York, NY, USA, 2005. ACM Press.

Digital Library

[5]

J. C. Culberson. On the futility of blind search: An algorithmic view of "no free lunch". Evolutionary Computation, 6(2):109--127, 1998.

Digital Library

[6]

S. Droste, T. Jansen, and I. Wegener. Optimization with randomized search heuristics - the (a)nfl theorem, realistic scenarios, and difficult functions. Theor. Comput. Sci., 287(1):131--144, 2002.

Digital Library

[7]

S. Droste, T. Jansen, and I. Wegener. Upper and lower bounds for randomized search heuristics in black-box optimization. Electronic Colloquium on Computational Complexity (ECCC), (048), 2003.

[8]

T. M. English. Evaluation of evolutionary and genetic optimizers: No free lunch. In Evolutionary Programming, pages 163--169, 1996.

[9]

T. M. English. On the structure of sequential search: Beyond "no free lunch". In J. Gottlieb and G. R. Raidl, editors, EvoCOP 2004, Coimbra, Portugal, April 5-7, 2004, Proceedings, volume 3004 of Lecture Notes in Computer Science, pages 95--103. Springer, 2004.

[10]

P. Grunwald and P. Vitanyi. Shannon information and kolmogorov complexity. IEEE Transactions on Information Theory, 2004. In Review.

[11]

C. Igel and M. Toussaint. A no-free-lunch theorem for non-uniform distributions of target functions. Journal of Mathematical Modelling and Algorithms, 3(4):313--322, 2004.

[12]

T. Jones and S. Forrest. Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Proceedings of the 6th International Conference on Genetic Algorithms, pages 184--192, San Francisco, CA, USA, 1995. Morgan Kaufmann Publishers Inc.

Digital Library

[13]

A. Moraglio and R. Poli. Topological interpretation of crossover. In K. Deb et al. editors, Genetic and Evolutionary Computation - GECCO 2004, Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004, Proceedings, Part I, volume 3102 of Lecture Notes in Computer Science, pages 1377--1388. Springer, 2004.

[14]

A. Moraglio and R. Poli. Abstract geometric crossover for the permutation representation. IEEE Trans. Evolutionary Computation, page submitted, 2005.

[15]

A. Moraglio and R. Poli. Geometric landscape of homologous crossover for syntactic trees. In CEC (1), pages 427--434, 2005.

[16]

P.F.Stadler and C.R.Stephens. Landscapes and effective fitness. Comments Theori. Biol., 8:389--431, 2002.

[17]

C. M. Reidys and P. F. Stadler. Combinatorial landscapes. SIAM Rev., 44(1):3--54, 2002.

Digital Library

[18]

J. Rowe, D. Whitley, L. Barbulescu, and J.-P. Watson. Properties of gray and binary representations. Evol. Comput., 12(1):47--76, 2004.

Digital Library

[19]

J. E. Rowe, M. D. Vose, and A. H. Wright. Group properties of crossover and mutation. Evol. Comput., 10(2):151--184, 2002.

Digital Library

[20]

J. E. Rowe, M. D. Vose, and A. H. Wright. Structural search spaces and genetic operators. Evol. Comput., 12(4):461--493, 2004.

Digital Library

[21]

C. Schumacher, M. D. Vose, and L. D. Whitley. The no free lunch and problem description length. In L. Spector et al. editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 565--570, San Francisco, California, USA, 7-11 July 2001. Morgan Kaufmann.

[22]

M. D. Vose. The Simple Genetic Algorithm: Foundations and Theory. MIT Press, Cambridge, MA, USA, 1998.

Digital Library

[23]

I. Wegener. Towards a theory of randomized search heuristics. In B. Rovan and P. Vojtás, editors, MFCS, volume 2747 of Lecture Notes in Computer Science, pages 125--141. Springer, 2003.

[24]

I. Wegener. Randomized search heuristics as an alternative to exact optimization. In W. Lenski, editor, Logic versus Approximation, Essays Dedicated to Michael M. Richter on the Occasion of his 65th Birthday, volume 3075 of Lecture Notes in Computer Science, pages 138--149. Springer, 2004.

[25]

D. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Trans. Evolutionary Computation, 1(1):67--82, 1997.

Digital Library

Cited By

Rajwar KDeep K(2025)Structural bias in metaheuristic algorithms: Insights, open problems, and future prospectsSwarm and Evolutionary Computation10.1016/j.swevo.2024.10181292(101812)Online publication date: Feb-2025
https://doi.org/10.1016/j.swevo.2024.101812
Hao GLim MOng YHuang HWang G(2019)Domination landscape in evolutionary algorithms and its applicationsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3206-x23:11(3563-3570)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3206-x
Rada RDu J(2014)Knowledge in Memetic Algorithms for Stock ClassificationInternational Journal of Artificial Life Research10.4018/ijalr.20140101024:1(13-29)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.4018/ijalr.2014010102
Show More Cited By

Index Terms

Structure and metaheuristics
1. Theory of computation
  1. Design and analysis of algorithms

Recommendations

An empirical study into the structure of heuristic combinations in an evolutionary algorithm hyper-heuristic for the examination timetabling problem
SAICSIT '10: Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists

A hyper-heuristic for the examination timetabling problem searches a space of constructive heuristic combinations instead of a space of examination timetables. The most optimal heuristic combination found by the search is used to construct the ...
GRASP with Path Relinking for Three-Index Assignment

This paper proposes and tests variants of GRASP (greedy randomized adaptive search procedure) with path relinking for the three-index assignment problem (AP3). GRASP is a multistart metaheuristic for combinatorial optimization. It usually consists of a ...
Local search versus Path Relinking in metaheuristics

Display Omitted Meta-RaPS is a recent metaheuristic for optimization problems that is simple and effective.The time consuming local search phase of Meta-RaPS is replaced by Path Relinking as a learning module.The results show that the newly designed ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Copyright © 2006 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

GECCO06

Sponsor:

GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

Acceptance Rates

GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
24
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rajwar KDeep K(2025)Structural bias in metaheuristic algorithms: Insights, open problems, and future prospectsSwarm and Evolutionary Computation10.1016/j.swevo.2024.10181292(101812)Online publication date: Feb-2025
https://doi.org/10.1016/j.swevo.2024.101812
Hao GLim MOng YHuang HWang G(2019)Domination landscape in evolutionary algorithms and its applicationsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3206-x23:11(3563-3570)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3206-x
Rada RDu J(2014)Knowledge in Memetic Algorithms for Stock ClassificationInternational Journal of Artificial Life Research10.4018/ijalr.20140101024:1(13-29)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.4018/ijalr.2014010102
Du JWimmer HRada R(2014)Knowledge constrained evolutionary algorithmsInternational Journal of Artificial Intelligence and Soft Computing10.1504/IJAISC.2014.0658014:4(335-353)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1504/IJAISC.2014.065801
Jansen T(2013)Black-Box Complexity for Bounding the Performance of Randomized Search HeuristicsTheory and Principled Methods for the Design of Metaheuristics10.1007/978-3-642-33206-7_5(85-110)Online publication date: 12-Nov-2013
https://doi.org/10.1007/978-3-642-33206-7_5
Gutjahr W(2010)Stochastic Search in MetaheuristicsHandbook of Metaheuristics10.1007/978-1-4419-1665-5_19(573-597)Online publication date: 12-Aug-2010
https://doi.org/10.1007/978-1-4419-1665-5_19
Wilson DKaur D(2009)Search, neutral evolution, and mapping in evolutionary computingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2008.200906313:3(566-590)Online publication date: 1-Jun-2009
https://dl.acm.org/doi/10.1109/TEVC.2008.2009063
Borenstein YPoli RCattolico M(2006)Classes of problems in the black box scenarioProceedings of the 8th annual conference on Genetic and evolutionary computation10.1145/1143997.1144215(1401-1402)Online publication date: 8-Jul-2006
https://dl.acm.org/doi/10.1145/1143997.1144215
Borenstein YPoli RCattolico M(2006)Structure and metaheuristicsProceedings of the 8th annual conference on Genetic and evolutionary computation10.1145/1143997.1144172(1087-1094)Online publication date: 8-Jul-2006
https://dl.acm.org/doi/10.1145/1143997.1144172

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents