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Opposition-Based Memetic Algorithm and Hybrid Approach for Sorting Permutations by Reversals.

José Luis Soncco-Álvarez1, Daniel M Muñoz2, Mauricio Ayala-Rincón3

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Summary
This summary is machine-generated.

Two novel memetic algorithms were developed to efficiently sort unsigned permutations by reversals. These algorithms, opposition-based memetic algorithm (OBMA) and hybrid OBMA, show superior performance in practical computational biology scenarios.

Keywords:
Sorting by reversalsgenetic algorithmsheuristics.memetic algorithmsopposition-based learning

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Algorithm Design

Background:

  • Sorting unsigned permutations by reversals is an NP-hard problem with significant implications in genome rearrangements.
  • Existing approximation algorithms have achieved a theoretical ratio of 1.375, but practical efficiency remains a challenge.

Purpose of the Study:

  • To propose and evaluate two novel memetic algorithms for computing the reversal distance of unsigned permutations.
  • To assess the performance of these algorithms, particularly for biological data and permutations up to length 120.

Main Methods:

  • Development of an opposition-based memetic algorithm (OBMA).
  • Enhancement of OBMA by incorporating a two-breakpoint elimination heuristic, creating a hybrid OBMA (Hybrid-OBMA).
  • Experimental validation using random, benchmark, and biological permutations.

Main Results:

  • Both OBMA and Hybrid-OBMA demonstrate superior practical performance for permutations up to length 120.
  • Hybrid-OBMA shows improved results over OBMA for permutations of length 60 and greater.
  • OBMA achieved the best average results across all tested biological permutation instances.

Conclusions:

  • The proposed OBMA and Hybrid-OBMA algorithms offer efficient solutions for the computationally challenging problem of sorting unsigned permutations by reversals.
  • These algorithms are particularly effective for practical applications in computational biology, outperforming existing methods for moderate-sized permutations.