An improved algorithm for the longest common subsequence problem

Mousavi, Seyed Rasoul; Tabataba, Farzaneh Sadat

doi:10.1016/j.cor.2011.02.026

Cited by 34 publications

(35 citation statements)

References 17 publications

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“…In this section, we explain how candidate solutions are evaluated and compared. The method used here to evaluate candidate solutions is adapted from Mousavi and Tabataba (2012) where a similar problem, the LCS, was addressed. To evaluate a candidate solution x, we use the probability of R(x)!y, where y is a random string and the strings in R(x) are assumed to be independent in the sense that Prðr i ðxÞ!yÞ ¼ Prðr i ðxÞ!y9r j ðxÞ!yÞ,8i,j A f1,.…”

Section: Evaluation Of Candidate Solutionsmentioning

confidence: 99%

“…Its time complexity is polynomial in input size, and its computational cost can be arbitrarily reduced by reducing the beam size. The heuristic function used in the algorithm to evaluate candidate solutions does not suffer from the scalability issue of the heuristic proposed in Mousavi and Tabataba (2012) as an estimation mechanism is used for long strings. While the algorithm is significantly faster than other recent algorithm for the SCS, it yields superior solution quality in most of the cases.…”

Section: Introductionmentioning

confidence: 99%

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An enhanced beam search algorithm for the Shortest Common Supersequence Problem

Mousavi

Bahri

Tabataba

2012

Engineering Applications of Artificial Intelligence

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a b s t r a c tThe Shortest Common Supersequence Problem asks to obtain a shortest string that is a supersequence of every member of a given set of strings. It has applications, among others, in data compression and oligonucleotide microarray production. The problem is NP-hard, and the existing exact solutions are impractical for large instances. In this paper, a new beam search algorithm is proposed for the problem, which employs a probabilistic heuristic and uses the dominance property to further prune the search space. The proposed algorithm is compared with three recent algorithms proposed for the problem on both random and biological sequences, outperforming them all by quickly providing solutions of higher average quality in all the experimental cases. The Java source and binary files of the proposed IBS_SCS algorithm and our implementation of the DR algorithm and all the random and real datasets used in this paper are freely available upon request.

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Section: Evaluation Of Candidate Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An enhanced beam search algorithm for the Shortest Common Supersequence Problem

Mousavi

Bahri

Tabataba

2012

Engineering Applications of Artificial Intelligence

Self Cite

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“…For arbitrary number of sequences, the problem of computing an LCS is NP-hard [28]. As a result a number of heuristic and/or meta-heuristic techniques have also been applied in the literature to solve the LCS problem for arbitrary number of sequences (e.g., [7,31,34,35]). The related problem of global alignment, which is more relevant in computational biology, has also received much attention in the literature.…”

Section: Introductionmentioning

confidence: 99%

Constrained sequence analysis algorithms in computational biology

Farhana

Rahman

2015

Information Sciences

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“…This implies that the problem can be solved in polynomial time if the number of sequences m is a constant, in particular it can be solved in quadratic time for two sequences. The complexity of the general problem motivates the use of heuristic algorithms and developing fast heuristics for the longest common subsequence problem is still an active research topic [24,26,27]. One heuristic approach employing evolutionary algorithms reports that evolutionary algorithms yield excellent results in practice [13,20].…”

Section: Introductionmentioning

confidence: 99%

Computing Longest Common Subsequences with the B-Cell Algorithm

Jansen

Zarges

2012

Lecture Notes in Computer Science

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Abstract. Computing a longest common subsequence of a number of strings is a classical combinatorial optimisation problem with many applications in computer science and bioinformatics. It is a hard problem in the general case so that the use of heuristics is motivated. Evolutionary algorithms have been reported to be successful heuristics in practice but a theoretical analysis has proven that a large class of evolutionary algorithms using mutation and crossover fail to solve and even approximate the problem efficiently. This was done using hard instances. We reconsider the very same hard instances and prove that the B-cell algorithm outperforms these evolutionary algorithms by far. The advantage stems from the use of contiguous hypermutations. The result is another demonstration that relatively simple artificial immune systems can excel over more complex evolutionary algorithms in the domain of optimisation.

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An improved algorithm for the longest common subsequence problem

Cited by 34 publications

References 17 publications

An enhanced beam search algorithm for the Shortest Common Supersequence Problem

An enhanced beam search algorithm for the Shortest Common Supersequence Problem

Constrained sequence analysis algorithms in computational biology

Computing Longest Common Subsequences with the B-Cell Algorithm

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