The longest common subsequence problem for arc-annotated sequences

Arc-annotated sequences are useful in representing the structural information of RNA and protein sequences. The

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problem has recently been introduced in [P.A. Evans, Algorithms and complexity for annotated sequence analysis, PhD Thesis, University of Victoria, 1999; P.A. Evans, Finding common subsequences with arcs and pseudoknots, in: Proceedings of 10th Annual Symposium on Combinatorial Pattern Matching (CPM'99), in: Lecture Notes in Comput. Sci., vol. 1645, 1999, pp. 270–280] as a framework for studying the similarity of arc-annotated sequences. In this paper, we consider arc-annotated sequences with various arc structures and present some new algorithmic and complexity results on the problem. Some of our results answer an open question in [P.A. Evans, Algorithms and complexity for annotated sequence analysis, PhD Thesis, University of Victoria, 1999; P.A. Evans, Finding common subsequences with arcs and pseudoknots, in: Proceedings of 10th Annual Symposium on Combinatorial Pattern Matching (CPM'99), in: Lecture Notes in Comput. Sci., vol. 1645, 1999, pp. 270–280] and some others improve the hardness results in [P.A. Evans, Algorithms and complexity for annotated sequence analysis, PhD Thesis, University of Victoria, 1999; P.A. Evans, Finding common subsequences with arcs and pseudoknots, in: Proceedings of 10th Annual Symposium on Combinatorial Pattern Matching (CPM'99), in Lecture Notes in Comput. Sci., vol. 1645, 1999, pp. 270–280].     

A large neighborhood search heuristic for the longest common subsequence problem

Given a set S={S ¹,…,S ^k } of finite strings, the k-Longest Common Subsequence Problem (k-LCSP) seeks a string L ^* of maximum length such that L ^* is a subsequence of each S ⁱ for i=1,…,k. This paper presents a large neighborhood search technique that provides quality solutions to large k-LCSP instances. This heuristic runs in linear time in both the length of the sequences and the number of sequences. Some computational results are provided.     

A hybrid genetic algorithm for the repetition free longest common subsequence problem

Computing the longest common subsequence of two sequences is one of the most studied algorithmic problems. In this work we focus on a particular variant of the problem, called repetition free longest common subsequence (RF-LCS$\text{<small-caps>RF-LCS</small-caps>}$), which has been proved to be NP-hard. We propose a hybrid genetic algorithm, which combines standard genetic algorithms and estimation of distribution algorithms, to solve this problem. An experimental comparison with some well-known approximation algorithms shows the suitability of the proposed technique.     

Repetition-free longest common subsequence

We study the problem of, given two finite sequences x and y, finding a repetition-free longest common subsequence of x and y. We show some algorithmic results, a complexity result, and a preliminary experimental study based on the proposed algorithms.     

Repetition-free longest common subsequence

We study the following problem. Given two sequences x and y over a finite alphabet, find a repetition-free longest common subsequence of x and y. We show several algorithmic results, a computational complexity result, and we describe a preliminary experimental study based on the proposed algorithms. We also show that this problem is APX-hard.     

Efficient algorithms for the longest common subsequence problem with sequential substring constraints

In this paper, we generalize the inclusion constrained longest common subsequence (CLCS) problem to the hybrid CLCS problem which is the combination of the sequence inclusion CLCS and the string inclusion CLCS, called the sequential substring constrained longest common subsequence   (SSCLCS) problem. In the SSCLCS problem, we are given two strings A$A$ and B$B$ of lengths m$m$ and n$n$, respectively, formed by alphabet Σ$\Sigma$ and a constraint sequence C$C$ formed by ordered strings (C¹,C²,C³,…,C^l)$(C^1,C^2,C^3,\dots ,C^l)$ with total length r$r$. The problem is that of finding the longest common subsequence D$D$ of A$A$ and B$B$ containing C¹,C²,C³,…,C^l$C^1,C^2,C^3,\dots ,C^l$ as substrings and with the order of the C$C$’s retained. This problem has two variants, depending on whether the strings in C$C$ cannot overlap or may overlap. We propose algorithms with O(mnl+(m+n)(|Σ|+r))$O(mnl+(m+n)(\left|\Sigma \right|+r))$ and O(mnr+(m+n)|Σ|)$O(mnr+(m+n)\left|\Sigma \right|)$ time for the two variants. For the special case with one or two constraints, our algorithm runs in O(mn+(m+n)(|Σ|+r))$O(mn+(m+n)(\left|\Sigma \right|+r))$ or O(mnr+(m+n)|Σ|)$O(mnr+(m+n)\left|\Sigma \right|)$ time, respectively—an order faster than the algorithm proposed by Chen and Chao.     

On the longest common increasing binary subsequence

Let $X_1,X_2,\dots$ and $Y_1,Y_2,\dots$ be two independent sequences of iid Bernoulli random variables with parameter 1/2. Let ${\mathit{LCI}}_n$ be the length of the longest increasing sequence which is a subsequence of both finite sequences $X_1,\dots ,X_n$ and $Y_1,\dots ,Y_n$. We prove that, as n goes to infinity, $n^{?1/2}({\mathit{LCI}}_n?n/2)$ converges in law to a Brownian functional that we identify. To cite this article: C. Houdré et al., C. R. Acad. Sci. Paris, Ser. I 343 (2006).     

Expected length of the longest common subsequence for large alphabets

We consider the length L of the longest common subsequence of two randomly uniformly and independently chosen n character words over a k-ary alphabet. Subadditivity arguments yield that E[L]/n converges to a constant γ_k. We prove a conjecture of Sankoff and Mainville from the early 1980s claiming that as k→∞.     

Computing a longest common subsequence for a set of strings

The known 2-string LCS problem is generalized to finding a Longest Common Subsequence (LCS) for a set of strings. A new, general approach that systematically enumerates common subsequences is proposed for the solution. Assuming a finite symbol set, it is shown that the presented scheme requires a preprocessing time that grows linearly with the total length of the input strings and a processing time that grows linearly with (K), the number of strings, and () the number of matches among them. The only previous algorithm for the generalized LCS problem takesO(K·|S ₁|·|S ₂|·...|S _k |) execution time, where |S _i | denotes the length of the stringS _i . Since typically is a very small percentage of |S ₁|·|S ₂|·...·|S _k |, the proposed method may be considered to be much more efficient than the straightforward dynamic programming approach.     

A comparison of three metaheuristics for the workover rig routing problem

The workover rig routing problem (WRRP) is a variant of the Vehicle Routing Problem with Time Windows (VRPTW) and arises in the operations of onshore oil fields. In this problem, a set of workover rigs located at different positions must service oil wells requesting maintenance as soon as possible. When a well requires maintenance, its production is reduced or stopped for safety reasons and some workover rig must service it within a given deadline. It is therefore important to service the wells in a timely fashion in order to minimize the production loss. Whereas for classical VRPTWs the objective is to minimize route length, in the WRRP the objective is to minimize the total lost production, equal to the sum of arrival times at the wells, multiplied by production loss rates. The WRRP generalizes the Delivery Man Problem with Time Windows by considering multiple open vehicle routes and multiple depots. This paper compares three metaheuristics for the WRRP: an iterated local search, a clustering search, and an Adaptive Large Neighborhood Search (ALNS). All approaches, in particular ALNS, have yielded good solutions for instances derived from a real-life setting.     

A note on the longest matching consecutive subsequence

The longest matching consecutive subsequence plays an important role in information theory and molecular biology. We consider the Hausdorff dimension of the set of points whose rate of growth of the longest matching consecutive subsequence is almost equal to a class of monotonically increasing functions.     

Upper bounds for the expected length of a longest common subsequence of two binary sequences

Let f(n) be the expected length of a longest common subsequence of two random binary sequences of length n. It is known that the limit γ = lim_{n→ ∞} n^?1 f(n) exists. Improved upper bounds for γ are given using a new method.     

Linear time algorithm for the longest common repeat problem

Given a set of strings U={T₁,T₂,…,T_ℓ}, the longest common repeat problem is to find the longest common substring that appears at least twice in each string of U. We also consider reversed and reverse-complemented repeats as well as normal repeats. We present a linear time algorithm for the longest common repeat problem.     

A concentration bound for the longest increasing subsequence of a randomly chosen involution

In this short note we prove a concentration result for the length of the longest increasing subsequence (LIS) of a randomly and uniformly chosen involution of {1,…,s}.     

Heuristics and metaheuristics for the maximum diversity problem

This paper presents extensive computational experiments to compare 10 heuristics and 20 metaheuristics for the maximum diversity problem (MDP). This problem consists of selecting a subset of maximum diversity from a given set of elements. It arises in a wide range of real-world settings and we can find a large number of studies, in which heuristic and metaheuristic methods are proposed. However, probably due to the fact that this problem has been referenced under different names, we have only found limited comparisons with a few methods on some sets of instances. This paper reviews all the heuristics and metaheuristics for finding near-optimal solutions for the MDP. We present the new benchmark library MDPLIB, which includes most instances previously used for this problem, as well as new ones, giving a total of 315. We also present an exhaustive computational comparison of the 30 methods on the MDPLIB. Non-parametric statistical tests are reported in our study to draw significant conclusions.     

Hybrid metaheuristics for the profitable arc tour problem

The profitable arc tour problem is a variant in the vehicle routing problems. It is included in the family of the vehicle routing with profit problems in which a set of vehicle tours are constructed. The objective is to find a set of cycles in the vehicle tours that maximize the collection of profits minus travel costs, subject to constraints limiting the length of cycles that profit is available on arcs. To solve this variant we adopted two metaheuristics based on adaptive memory. We show that our algorithms provide good results in terms of solution quality and running times.     

Dynamic programming based metaheuristics for the dial-a-ride problem

The organization of a specialized transportation system to perform transports for elderly and handicapped people is usually modeled as dial-a-ride problem. Users place transportation requests with specified pickup and delivery locations and times. The requests have to be completed under user inconvenience considerations by a specified fleet of vehicles. In the dial-a-ride problem, the aim is to minimize the total travel times respecting the given time windows, the maximum user ride times, and the vehicle restrictions. This paper introduces a dynamic programming algorithm for the dial-a-ride problem and demonstrates its effective application in (hybrid) metaheuristic approaches. Compared to most of the works presented in literature, this approach does not make use of any (commercial) solver. We present an exact dynamic programming algorithm and a dynamic programming based metaheuristic, which restricts the considered solution space. Then, we propose a hybrid metaheuristic algorithm which integrates the dynamic programming based algorithms into a large neighborhood framework. The algorithms are tested on a given set of benchmark instances from the literature and compared to a state-of-the-art hybrid large neighborhood search approach.     

Comparing local search metaheuristics for the maximum diversity problem

The Maximum Diversity Problem (MDP) requires to extract a subset M of given cardinality from a set N, maximising the sum of the pair-wise diversities between the extracted elements. The MDP has recently been the subject of much research, and several sophisticated heuristics have been proposed to solve it. The present work compares four local search metaheuristics for the MDP, all based on the same Tabu Search procedure, with the aim to identify what additional elements provide the strongest improvement. The four metaheuristics are an Exploring Tabu Search, a Scatter Search, a Variable Neighbourhood Search and a simple Random Restart algorithm. All of them prove competitive with the best algorithms proposed in the literature. Quite surprisingly, the best ones are the simple Random Restart algorithm and a Variable Neighbourhood Search algorithm with an unusual parameter setting, which makes it quite close to random restart. Although this is probably related to the elementary structure of the MDP, it also suggests that, more often than expected, simpler algorithms might be better.