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A biased random-key genetic algorithm for the maximum quasi-clique problem

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  • Pinto, Bruno Q.
  • Ribeiro, Celso C.
  • Rosseti, Isabel
  • Plastino, Alexandre

Abstract

Given a graph G=(V,E) and a threshold γ ∈ (0, 1], the maximum cardinality quasi-clique problem consists in finding a maximum cardinality subset C* of the vertices in V such that the density of the graph induced in G by C* is greater than or equal to the threshold γ. This problem is NP-hard, since it admits the maximum clique problem as a special case. It has a number of applications in data mining, e.g. in social networks or phone call graphs. In this work, we propose a biased random-key genetic algorithm for solving the maximum cardinality quasi-clique problem. Two alternative decoders are implemented for the biased random-key genetic algorithm and the corresponding algorithm variants are evaluated. Computational results show that the newly proposed approaches improve upon other existing heuristics for this problem in the literature. All input data for the test instances and all detailed numerical results are available from Mendeley.

Suggested Citation

  • Pinto, Bruno Q. & Ribeiro, Celso C. & Rosseti, Isabel & Plastino, Alexandre, 2018. "A biased random-key genetic algorithm for the maximum quasi-clique problem," European Journal of Operational Research, Elsevier, vol. 271(3), pages 849-865.
    • Handle: RePEc:eee:ejores:v:271:y:2018:i:3:p:849-865
    DOI: 10.1016/j.ejor.2018.05.071
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    References listed on IDEAS

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    1. Alexander Veremyev & Oleg A. Prokopyev & Sergiy Butenko & Eduardo L. Pasiliao, 2016. "Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs," Computational Optimization and Applications, Springer, vol. 64(1), pages 177-214, May.
    2. Thiago Noronha & Mauricio Resende & Celso Ribeiro, 2011. "A biased random-key genetic algorithm for routing and wavelength assignment," Journal of Global Optimization, Springer, vol. 50(3), pages 503-518, July.
    3. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
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    Cited by:

    1. Zhou, Qing & Benlic, Una & Wu, Qinghua, 2020. "An opposition-based memetic algorithm for the maximum quasi-clique problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 63-83.
    2. Zhuqi Miao & Balabhaskar Balasundaram, 2020. "An Ellipsoidal Bounding Scheme for the Quasi-Clique Number of a Graph," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 763-778, July.

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