An A⁎ search algorithm for the constrained longest common subsequence problem

Djukanovic, Marko; Berger, Christoph; Raidl, Günther R.; Blum, Christian

doi:10.1016/j.ipl.2020.106041

Cited by 5 publications

(7 citation statements)

References 25 publications

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“…Various efficient exact approaches were proposed [6,14,15]. Recently, the (2, 1)-CLCS problem was tackled with an A * search [16] which can be considered the new stateof-the-art exact method for various real and artificial benchmark sets. The A * could solve the (2, 1)-CLCS instances up to n = 1000.…”

Section: Concerningmentioning

confidence: 99%

Graph search and variable neighborhood search for finding constrained longest common subsequences in artificial and real gene sequences

Djukanovic

Kartelj

Matić

et al. 2022

Applied Soft Computing

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Section: Concerningmentioning

confidence: 99%

Graph search and variable neighborhood search for finding constrained longest common subsequences in artificial and real gene sequences

Djukanovic

Kartelj

Matić

et al. 2022

Applied Soft Computing

Self Cite

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“…This section describes the state graph for the m-CLCS problem, in which paths from a dedicated root node to inner nodes correspond to (meaningful) partial solutions, paths from the root to sink nodes correspond to complete solutions, and directed arcs represent (meaningful) extensions of partial solutions. Note that the state graph for the m-CLCS problem is an extension of the state graph of the 2-CLCS problem [6].…”

Section: State Graph For the M-clcs Problemmentioning

confidence: 99%

“…This bound is admissible for the A * search, which means that its values never underestimate the optimal value of the subproblem that corresponds to a node v. Moreover, the bound is monotonic, that is, the estimated upper bound of any child node is never smaller than the upper bound of the parent node. Monotonicity is an important property in A * search, because it implies that no re-expansion of already expanded nodes [6] may occur.…”

Section: Upper Boundsmentioning

confidence: 99%

“…The most studied CLCS variant is the one with only two input strings (2-CLCS); see, for example, [2,5,14]. In addition to these works from the literature, we recently proposed an A * search for the 2-CLCS problem [6] and showed that this algorithm is approximately one order of magnitude faster than other exact approaches.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, the approximation algorithm by Gotthilf et al [9] is the only existing algorithm for solving the general m-CLCS problem so far. We first extend the general search framework and the A * search from [6] to solve the more general m-CLCS problem. For the application to large-scale instances we additionally propose two heuristic techniques: (i) a greedy heuristic that is efficient in producing reasonably good solutions within a short runtime, and (ii) a beam search (BS) which produces high-quality solutions at the cost of more time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On Solving a Generalized Constrained Longest Common Subsequence Problem

Djukanovic

Berger

Raidl

et al. 2020

Optimization and Applications

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Given a set of two input strings and a pattern string, the constrained longest common subsequence problem deals with finding a longest string that is a subsequence of both input strings and that contains the given pattern string as a subsequence. This problem has various applications, especially in computational biology. In this work we consider the N P-hard case of the problem in which more than two input strings are given. First, we adapt an existing A * search from two input strings to an arbitrary number m of input strings (m ≥ 2). With the aim of tackling large problem instances approximately, we additionally propose a greedy heuristic and a beam search. All three algorithms are compared to an existing approximation algorithm from the literature. Beam search turns out to be the best heuristic approach, matching almost all optimal solutions obtained by A * search for rather small instances.

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Application of A$$^*$$ to the Generalized Constrained Longest Common Subsequence Problem with Many Pattern Strings

Djukanovic

Matić

Blum

et al. 2022

Pattern Recognition and Artificial Intelligence

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This paper addresses the Restricted Longest Common Subsequence (RLCS) problem, an extension of the well-known Longest Common Subsequence (LCS) problem. This problem has significant applications in bioinformatics, particularly for identifying similarities and discovering mutual patterns and important motifs among DNA, RNA, and protein sequences. Building on recent advancements in solving this problem through a general search framework, this paper introduces two novel heuristic approaches designed to enhance the search process by steering it towards promising regions in the search space. The first heuristic employs a probabilistic model to evaluate partial solutions during the search process. The second heuristic is based on a neural network model trained offline using a genetic algorithm. A key aspect of this approach is extracting problem-specific features of partial solutions and the complete problem instance. An effective hybrid method, referred to as the learning beam search, is developed by combining the trained neural network model with a beam search framework. An important contribution of this paper is found in the generation of real-world instances where scientific abstracts serve as input strings, and a set of frequently occurring academic words from the literature are used as restricted patterns. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed approaches in solving the RLCS problem. Finally, an empirical explainability analysis is applied to the obtained results. In this way, key feature combinations and their respective contributions to the success or failure of the algorithms across different problem types are identified.

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An A⁎ search algorithm for the constrained longest common subsequence problem

Cited by 5 publications

References 25 publications

Graph search and variable neighborhood search for finding constrained longest common subsequences in artificial and real gene sequences

Graph search and variable neighborhood search for finding constrained longest common subsequences in artificial and real gene sequences

On Solving a Generalized Constrained Longest Common Subsequence Problem

Application of A$$^*$$ to the Generalized Constrained Longest Common Subsequence Problem with Many Pattern Strings

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