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Novel Sequence Discovery by Subtractive Genomics
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An Opposition-Based Learning CRO Algorithm for Solving the Shortest Common Supersequence Problem.

Fei Luo1, Cheng Chen1, Joel Fuentes2

  • 1School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

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|May 28, 2022
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Summary
This summary is machine-generated.

This study introduces OBLIMCRO, a novel algorithm combining opposition-based learning with improved chemical reaction optimization to solve the shortest common supersequence problem more efficiently. OBLIMCRO significantly reduces computation time for DNA and protein sequence analysis.

Keywords:
NP-hardchemical reaction optimizationheuristic algorithmopposition-based learningshortest common supersequence

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Algorithm Optimization

Background:

  • The shortest common supersequence (SCS) problem is NP-hard, typically addressed by heuristic and metaheuristic algorithms.
  • Chemical Reaction Optimization (CRO) is effective for SCS but suffers from population instability and premature convergence.
  • Existing CRO variants struggle with maintaining solution quality and avoiding local optima.

Purpose of the Study:

  • To enhance the performance of Chemical Reaction Optimization (CRO) for solving the shortest common supersequence (SCS) problem.
  • To address limitations of existing CRO algorithms, including unstable population quality and local optimum convergence.
  • To introduce a novel algorithm, OBLIMCRO, integrating opposition-based learning (OBL) with improved CRO (IMCRO).

Main Methods:

  • Developed OBLIMCRO by integrating opposition-based learning (OBL) into the improved chemical reaction optimization (IMCRO) algorithm.
  • Implemented an initialization phase using OBL to create an opposite population, selecting the best initial molecules from both random and opposite sets.
  • Utilized reaction operators in the iterative phase to generate new molecules and update the population.

Main Results:

  • OBLIMCRO demonstrated a significant reduction in average running time, exceeding 50% improvement compared to CRO_SCS and its baseline IMCRO.
  • The algorithm was tested on desoxyribonucleic acid (DNA) and protein datasets, confirming its efficiency.
  • OBLIMCRO effectively overcomes the limitations of unstable molecular population quality and uneven distribution seen in prior CRO methods.

Conclusions:

  • OBLIMCRO presents a superior approach for tackling the shortest common supersequence problem, particularly for biological sequence data.
  • The integration of OBL significantly enhances the efficiency and stability of CRO-based algorithms.
  • This optimized algorithm offers a faster and more robust solution for complex computational biology challenges.