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Sequence Classification via LCS

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Intelligent Decision Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 309))

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Abstract

Computing a longest common subsequence of two given sequences is a fundamental problem in several fields of computer science. While there exist dynamic programming algorithms that can solve the problem in polynomial-time, their running time is considered too high for some practical applications. In this contribution we propose a method for comparing two sequences that is based on (1) a combinatorial algorithm that computes a window constrained longest common subsequence and (2) a genetic algorithm. We present experiments on synthetic datasets that show that the method is able to return solutions close to the length of a longest compute common subsequence. Moreover, the method is faster than the dynamic programming algorithm for the longest common subsequence problem.

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Correspondence to Riccardo Dondi .

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Dondi, R. (2022). Sequence Classification via LCS. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_7

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