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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.

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