Ant colony optimization beats resampling on noisy functions

Tobias Friedrich; Timo Kötzing; Francesco Quinzan; Andrew M. Sutton

doi:10.1145/2908961.2909039

Ant colony optimization beats resampling on noisy functions

Tobias Friedrich, Timo Kötzing, Francesco Quinzan, Andrew M. Sutton

Computer Science (Duluth)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Despite the pervasiveness of noise in real-world optimization, there is little understanding of the interplay between the operators of randomized search heuristics and explicit noise-handling techniques such as statistical resampling. Ant Colony Optimization (ACO) algorithms are claimed to be particularly well-suited to dynamic and noisy problems, even without explicit noise-handling techniques. In this work, we empirically investigate the trade-offs between resampling an the noise-handling abilities of ACO algorithms. Our main focus is to locate the point where resampling costs more than it is worth.

Original language	English (US)
Title of host publication	GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
Editors	Tobias Friedrich
Publisher	Association for Computing Machinery, Inc
Pages	3-4
Number of pages	2
ISBN (Electronic)	9781450343237
DOIs	https://doi.org/10.1145/2908961.2909039
State	Published - Jul 20 2016
Event	2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States Duration: Jul 20 2016 → Jul 24 2016

Publication series

Name	GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

Other

Other	2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
Country/Territory	United States
City	Denver
Period	7/20/16 → 7/24/16

Bibliographical note

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

Ant Colony Optimization
Crossover
Estimation of Distribution Algorithm
Evolutionary Algorithm
Genetic Algorithm
Noise
Robustness

Publisher link

10.1145/2908961.2909039

Find It

Find It at U of M Libraries

Cite this

Friedrich, T., Kötzing, T., Quinzan, F., & Sutton, A. M. (2016). Ant colony optimization beats resampling on noisy functions. In T. Friedrich (Ed.), GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 3-4). (GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2908961.2909039

Ant colony optimization beats resampling on noisy functions. / Friedrich, Tobias; Kötzing, Timo; Quinzan, Francesco et al.
GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. ed. / Tobias Friedrich. Association for Computing Machinery, Inc, 2016. p. 3-4 (GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Friedrich, T, Kötzing, T, Quinzan, F & Sutton, AM 2016, Ant colony optimization beats resampling on noisy functions. in T Friedrich (ed.), GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, pp. 3-4, 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion, Denver, United States, 7/20/16. https://doi.org/10.1145/2908961.2909039

Friedrich T, Kötzing T, Quinzan F, Sutton AM. Ant colony optimization beats resampling on noisy functions. In Friedrich T, editor, GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc. 2016. p. 3-4. (GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference). doi: 10.1145/2908961.2909039

Friedrich, Tobias ; Kötzing, Timo ; Quinzan, Francesco et al. / Ant colony optimization beats resampling on noisy functions. GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. editor / Tobias Friedrich. Association for Computing Machinery, Inc, 2016. pp. 3-4 (GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference).

@inproceedings{85bd1e8f30b647928b4c69a085713e33,

title = "Ant colony optimization beats resampling on noisy functions",

abstract = "Despite the pervasiveness of noise in real-world optimization, there is little understanding of the interplay between the operators of randomized search heuristics and explicit noise-handling techniques such as statistical resampling. Ant Colony Optimization (ACO) algorithms are claimed to be particularly well-suited to dynamic and noisy problems, even without explicit noise-handling techniques. In this work, we empirically investigate the trade-offs between resampling an the noise-handling abilities of ACO algorithms. Our main focus is to locate the point where resampling costs more than it is worth.",

keywords = "Ant Colony Optimization, Crossover, Estimation of Distribution Algorithm, Evolutionary Algorithm, Genetic Algorithm, Noise, Robustness",

author = "Tobias Friedrich and Timo K{\"o}tzing and Francesco Quinzan and Sutton, {Andrew M.}",

note = "Publisher Copyright: {\textcopyright} 2016 Copyright held by the owner/author(s). Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion ; Conference date: 20-07-2016 Through 24-07-2016",

year = "2016",

month = jul,

day = "20",

doi = "10.1145/2908961.2909039",

language = "English (US)",

series = "GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery, Inc",

pages = "3--4",

editor = "Tobias Friedrich",

booktitle = "GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",

}

TY - GEN

T1 - Ant colony optimization beats resampling on noisy functions

AU - Friedrich, Tobias

AU - Kötzing, Timo

AU - Quinzan, Francesco

AU - Sutton, Andrew M.

PY - 2016/7/20

Y1 - 2016/7/20

N2 - Despite the pervasiveness of noise in real-world optimization, there is little understanding of the interplay between the operators of randomized search heuristics and explicit noise-handling techniques such as statistical resampling. Ant Colony Optimization (ACO) algorithms are claimed to be particularly well-suited to dynamic and noisy problems, even without explicit noise-handling techniques. In this work, we empirically investigate the trade-offs between resampling an the noise-handling abilities of ACO algorithms. Our main focus is to locate the point where resampling costs more than it is worth.

AB - Despite the pervasiveness of noise in real-world optimization, there is little understanding of the interplay between the operators of randomized search heuristics and explicit noise-handling techniques such as statistical resampling. Ant Colony Optimization (ACO) algorithms are claimed to be particularly well-suited to dynamic and noisy problems, even without explicit noise-handling techniques. In this work, we empirically investigate the trade-offs between resampling an the noise-handling abilities of ACO algorithms. Our main focus is to locate the point where resampling costs more than it is worth.

KW - Ant Colony Optimization

KW - Crossover

KW - Estimation of Distribution Algorithm

KW - Evolutionary Algorithm

KW - Genetic Algorithm

KW - Noise

KW - Robustness

UR - http://www.scopus.com/inward/record.url?scp=84986281464&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84986281464&partnerID=8YFLogxK

U2 - 10.1145/2908961.2909039

DO - 10.1145/2908961.2909039

M3 - Conference contribution

AN - SCOPUS:84986281464

T3 - GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

SP - 3

EP - 4

BT - GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

A2 - Friedrich, Tobias

PB - Association for Computing Machinery, Inc

T2 - 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion

Y2 - 20 July 2016 through 24 July 2016

ER -

Ant colony optimization beats resampling on noisy functions

Abstract

Publication series

Other

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this