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A Self-adaptive Differential Evolution with Fragment Insertion for the Protein Structure Prediction Problem

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Hybrid Metaheuristics (HM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11299))

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Abstract

This work presents four hybrid methods based on the Self-adaptive Differential Evolution algorithm with fragment insertion applied to the protein structure prediction problem. The protein representation is the backbone torsion angles with side chain centroid coordinates. The fragment insertion is made by the Monte Carlo algorithm. The hybrid methods were compared with recent and compatible methods from the literature, where two proposed approaches achieved competitive results. The results have shown that using parameter control and fragment insertion greatly improves the results of the prediction when compared to fragment-less methods or without parameter control. Furthermore, an extra analysis was conducted using GDT-TS and TM-Score metrics to better understand the results obtained.

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Authors and Affiliations

  1. Graduate Program in Applied Computing, Santa Catarina State University, Joinville, SC, Brazil

    Renan S. Silva & Rafael Stubs Parpinelli

Authors
  1. Renan S. Silva
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  2. Rafael Stubs Parpinelli
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Corresponding author

Correspondence to Rafael Stubs Parpinelli .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa Aguilera

  2. Spanish National Research Council, Bellaterra, Spain

    Christian Blum

  3. Universidade Federal de Ouro Preto, Ouro Preto, Brazil

    Haroldo Gambini Santos

  4. University of Concepción, Concepción, Chile

    Pedro Pinacho-Davidson

  5. Universidad de Concepción, Concepción, Chile

    Julio Godoy del Campo

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Silva, R.S., Stubs Parpinelli, R. (2019). A Self-adaptive Differential Evolution with Fragment Insertion for the Protein Structure Prediction Problem. In: Blesa Aguilera, M., Blum, C., Gambini Santos, H., Pinacho-Davidson, P., Godoy del Campo, J. (eds) Hybrid Metaheuristics. HM 2019. Lecture Notes in Computer Science(), vol 11299. Springer, Cham. https://doi.org/10.1007/978-3-030-05983-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-05983-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05982-8

  • Online ISBN: 978-3-030-05983-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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