research-article

A hybrid incremental genetic algorithm for subgraph isomorphism problem

Authors:

Byung-Ro MoonAuthors Info & Claims

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

Pages 445 - 452

https://doi.org/10.1145/2576768.2598382

Published: 12 July 2014 Publication History

Abstract

Finding an isomorphic subgraph is a key problem in many real world applications modeled on graph. In this paper, we propose a new hybrid genetic algorithm(GA) for subgraph isomorphism problem which uses an incremental approach. We solve the problem with increasing the size of the subproblem step by step. The graph for which we search is gradually expanded from the empty structure to the entire one. We apply a hybrid GA to each subproblem, initialized with the evolved population of previous step. We present design issues for the incremental approach, and the effects of each design decision are analyzed by experiment. The proposed algorithm is tested on widely used dataset. With apposite vertex reordering along with moderate population diversity, incremental approach brought a significant performance improvement. Experimental results showed that our algorithm outperformed representative previous works.

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Cited By

Pachuau JSingh NSingh LSaha A(2023)Solving Dense Subgraph Problems Using Genetic AlgorithmTENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)10.1109/TENCON58879.2023.10322484(674-679)Online publication date: 31-Oct-2023
https://doi.org/10.1109/TENCON58879.2023.10322484
Jimenez-Aparicio AMezura-Montes EAcosta-Mesa H(2021)Evolutionary Algorithms for Searching Almost-Equienergetic Graphs2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504751(1093-1098)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9504751
Rezapoor Mirsaleh MMeybodi M(2017)Balancing exploration and exploitation in memetic algorithms: A learning automata approachComputational Intelligence10.1111/coin.1214834:1(282-309)Online publication date: 9-Oct-2017
https://doi.org/10.1111/coin.12148
Show More Cited By

Index Terms

A hybrid incremental genetic algorithm for subgraph isomorphism problem
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
  2. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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cover image ACM Conferences

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

July 2014

1478 pages

ISBN:9781450326629

DOI:10.1145/2576768

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

Copyright © 2014 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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

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Research-article

Funding Sources

Ministry of Science, ICT and Future Planning

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GECCO '14

Sponsor:

SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

Acceptance Rates

GECCO '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

Pachuau JSingh NSingh LSaha A(2023)Solving Dense Subgraph Problems Using Genetic AlgorithmTENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)10.1109/TENCON58879.2023.10322484(674-679)Online publication date: 31-Oct-2023
https://doi.org/10.1109/TENCON58879.2023.10322484
Jimenez-Aparicio AMezura-Montes EAcosta-Mesa H(2021)Evolutionary Algorithms for Searching Almost-Equienergetic Graphs2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504751(1093-1098)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9504751
Rezapoor Mirsaleh MMeybodi M(2017)Balancing exploration and exploitation in memetic algorithms: A learning automata approachComputational Intelligence10.1111/coin.1214834:1(282-309)Online publication date: 9-Oct-2017
https://doi.org/10.1111/coin.12148
Kim JYoon YMoon BNeumann FSutton A(2016)Solving Maximum Cut Problem with an Incremental Genetic AlgorithmProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2908976(49-50)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2908976
Kim JChoi HYun HMoon BNeumann FSutton A(2016)Measuring Source Code Similarity by Finding Similar Subgraph with an Incremental Genetic AlgorithmProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908870(925-932)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908870

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