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Parameter tuning in quantum-inspired evolutionary algorithms for partitioning complex networks

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GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 1045 - 1048

https://doi.org/10.1145/2598394.2605672

Published: 12 July 2014 Publication History

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Abstract

We propose a numeric variant of quantum-inspired evolutionary algorithm (QIEA) where gene in the quantum chromosome is a superposition of k qubits, thus allowing the genes of the classical chromosome to take numeric values. We also present a modified form of real observation QIEA. Both these techniques are applied to the problem of partitioning a complex network. The algorithm parameters are tuned using an evolutionary bilevel search optimization technique.

References

[1]

L. Danon, A. Diaz-Guilera, J. Duch, and A. Arenas. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09):P09008, 2005.

Crossref

Google Scholar

[2]

K. Deb and A. Sinha. Solving bilevel multi-objective optimization problems using evolutionary algorithms. In Evolutionary Multi-Criterion Optimization, pages 110--124. Springer, 2009.

Digital Library

Google Scholar

[3]

K.-H. Han and J.-H. Kim. Genetic quantum algorithm and its application to combinatorial optimization problem. In Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, volume 2, pages 1354--1360. IEEE, 2000.

Google Scholar

[4]

E. A. Leicht and M. E. Newman. Community structure in directed networks. Physical review letters, 100(11):118703, 2008.

Google Scholar

[5]

D. Lusseau. The emergent properties of a dolphin social network. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(Suppl 2):S186--S188, 2003.

Google Scholar

[6]

M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review, E 69(026113), 2004.

Google Scholar

[7]

G. Ochoa. Error thresholds and optimal mutation rates in genetic algorithms. PhD thesis, 2000.

Google Scholar

[8]

A. Seshadri. Nsga - ii: A multi-objective optimization algorithm, July 2009.

Google Scholar

[9]

M. Tasgin and H. Bingol. Community detection in complex networks using genetic algorithm. In ECCS '06: Proc. of the European Conference on Complex Systems, Apr. 2006.

Google Scholar

[10]

L. Wang, H. Wu, F. Tang, and D.-Z. Zheng. A hybrid quantum-inspired genetic algorithm for flow shop scheduling. In Advances in Intelligent Computing, pages 636--644. Springer, 2005.

Digital Library

Google Scholar

[11]

W. W. Zachary. An information flow model for conflict and fission in small groups. Journal of anthropological research, pages 452--473, 1977.

Google Scholar

[12]

G. Zhang. Quantum-inspired evolutionary algorithms: a survey and empirical study. Journal of Heuristics, 17(3):303--351, 2011.

Digital Library

Google Scholar

Cited By

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Akbar SSaritha S(2021)Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate changeScientific Reports10.1038/s41598-021-93122-x11:1Online publication date: 12-Jul-2021
https://doi.org/10.1038/s41598-021-93122-x
Akbar SSaritha S(2020)QML Based Community Detection in the realm of Social Network Analysis2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT49239.2020.9225271(1-7)Online publication date: Jul-2020
https://doi.org/10.1109/ICCCNT49239.2020.9225271
Akbar SSaritha S(2020)Towards quantum computing based community detectionComputer Science Review10.1016/j.cosrev.2020.10031338(100313)Online publication date: Nov-2020
https://doi.org/10.1016/j.cosrev.2020.100313

Index Terms

Parameter tuning in quantum-inspired evolutionary algorithms for partitioning complex networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

Acceptance Rates

GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

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

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Cited By

View all

Akbar SSaritha S(2021)Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate changeScientific Reports10.1038/s41598-021-93122-x11:1Online publication date: 12-Jul-2021
https://doi.org/10.1038/s41598-021-93122-x
Akbar SSaritha S(2020)QML Based Community Detection in the realm of Social Network Analysis2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT49239.2020.9225271(1-7)Online publication date: Jul-2020
https://doi.org/10.1109/ICCCNT49239.2020.9225271
Akbar SSaritha S(2020)Towards quantum computing based community detectionComputer Science Review10.1016/j.cosrev.2020.10031338(100313)Online publication date: Nov-2020
https://doi.org/10.1016/j.cosrev.2020.100313

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