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Heuristic Search Space Generation for Maximum Clique Problem Inspired in Biomolecular Filtering

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Abstract

Biomolecular filtering, as a computing paradigm, consists essentially of two steps: (1) the codification of candidate solutions in DNA and (2) the removal of non-valid solutions by means of biochemical procedures. The sticker model makes use of this notion, defining simple bitwise operations over large sets of DNA-coded binary strings. A sticker machine is conceived as a robotic station automatizing sticker operations and thus, can be seen as an SIMD computer with a densely populated pool of data. In this paper, the maximum clique problem is tackled by harnessing the massive threading of the CUDA SIMT architecture to replicate the parallel strand filtering. The proposed heuristic relies on the sequential-and-progressive generation of partial search spaces for subsequent parallel filtering in GPU. Computational results over DIMACS benchmark set show that our approach is competitive, compared to a preceding similar work and to state-of-the-art branch-and-bound algorithms. Moreover, our approach is scalable and produces more than one solution for some instances. As far as we know, the number of found cliques has not been previously used as a reference point for this problem.

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Acknowledgements

This research was supported by CONACYT under grant SEP-CONACYT-CB-2010-01-154863 and the CONACYT fellowship to NEOG.

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  1. Computer Sciences Department, CICESE Research Center, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, C.P. 22860, Ensenada, B.C., Mexico

    Nelson E. Ordóñez-Guillén & Israel M. Martínez-Pérez

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  1. Nelson E. Ordóñez-Guillén
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  2. Israel M. Martínez-Pérez
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Correspondence to Israel M. Martínez-Pérez.

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Ordóñez-Guillén, N.E., Martínez-Pérez, I.M. Heuristic Search Space Generation for Maximum Clique Problem Inspired in Biomolecular Filtering. J Sign Process Syst 83, 389–400 (2016). https://doi.org/10.1007/s11265-015-1027-z

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  • DOI: https://doi.org/10.1007/s11265-015-1027-z

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