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A Swarm Intelligence Heuristic Approach to Longest Common Subsequence Problem for Arbitrary Number of Sequences

Abstract

Ali Teoman Unay and Meral Guzey

Personalized cancer care strategies involving sequencing requires accuracy. We aimed to develop a novel
approach to solve the longest common subsequence problem, which is a common computer science problem in the field of bioinformatics to facilitate the next generation sequencing of cancer biomarkers. We are using particle swarm optimization heuristic technique, which uses a novel “Occurrence Listing” (OL) technique as the evaluation function. This aims to keep lists of the sequence elements and offers criteria to evaluate randomly generated population of sequences.