A Hybrid Evolutionary Algorithm Based on Solution Merging for the Longest Arc-Preserving Common Subsequence Problem
This work addresses a specific problem in RNA sequence comparison, but it is incremental as it builds on prior methods with a novel crossover approach.
The authors tackled the NP-hard longest arc-preserving common subsequence problem in computational biology by proposing a hybrid evolutionary algorithm with a crossover operator based on solution merging, which outperformed an existing heuristic in experiments.
The longest arc-preserving common subsequence problem is an NP-hard combinatorial optimization problem from the field of computational biology. This problem finds applications, in particular, in the comparison of arc-annotated Ribonucleic acid (RNA) sequences. In this work we propose a simple, hybrid evolutionary algorithm to tackle this problem. The most important feature of this algorithm concerns a crossover operator based on solution merging. In solution merging, two or more solutions to the problem are merged, and an exact technique is used to find the best solution within this union. It is experimentally shown that the proposed algorithm outperforms a heuristic from the literature.