Starting from Scratch: Growing Longest Common Subsequences with Evolution

Julstrom, Bryant A.; Hinkemeyer, Brenda

doi:10.1007/11844297_94

Cited by 9 publications

(23 citation statements)

References 11 publications

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“…This coincides with the empirical observation that evolutionary algorithms perform better if started with trivial empty candidate solutions [20]. However, we prove that for evolutionary algorithms the performance on the considered instances is still very bad even with this initialisation.…”

Section: Introductionsupporting

confidence: 82%

“…Moreover, one would like that infeasible solutions are assigned smaller values than feasible solutions. To this end Hinkemeyer and Julstrom [13,20] define a rather complicated function that we call f HJ here. Jansen and Weyland [18] consider this function and define two more, called f MAX and f LCS , respectively.…”

Section: Notation Algorithms Problem and Encodingmentioning

confidence: 99%

“…This problem encoding defines the search space S, i. e., the set of all candidate solutions, as well as an objective function f such that an optimal solution has maximal function value under f . We use the encoding defined by Jansen and Weyland [18], which in turn is motivated by the encoding used by Hinkemeyer and Julstrom [13,20].…”

Section: Notation Algorithms Problem and Encodingmentioning

confidence: 99%

“…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]. A subsequent theoretical analysis, however, revealed that the performance of a large class of evolutionary algorithms is extremely bad even for problem instances that are easy to solve using the problem-specific algorithm based on dynamic programming [18].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionsupporting

confidence: 82%

Section: Notation Algorithms Problem and Encodingmentioning

confidence: 99%

Section: Notation Algorithms Problem and Encodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computing Longest Common Subsequences with the B-Cell Algorithm

Jansen

Zarges

2012

Lecture Notes in Computer Science

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“…The body of work on approximate methods is dominated by constructive one-pass heuristics [9], [10]. Moreover, metaheuristics have been proposed in [17], [8], [12]. In [3] we recently published the current state-of-the-art algorithm for the LCS problem.…”

Section: Introductionmentioning

confidence: 99%

Beam-ACO for the longest common subsequence problem

Blum

2010

IEEE Congress on Evolutionary Computation

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Abstract-The longest common subsequence problem is classical string problem. It has applications, for example, in pattern recognition and bioinformatics. In this work we present a socalled Beam-ACO approach for solving this problem. Beam-ACO algorithms are hybrid techniques that results from a combination of ant colony optimization and beam search, which is an incomplete branch and bound derivative. Our results show that Beam-ACO is able to find new best solutions for 31 out of 60 benchmark instances that we chose for the experimental evaluation of the algorithm. I. INTRODUCTIONThe longest common subsequence (LCS) problem is one of the classical string problems. Given a problem instance (S, Σ), where S = {s 1 , s 2 , . . . , s n } is a set of n strings over a finite alphabet Σ, the problem consists in finding a longest string t * that is a subsequence of all the strings in S. Such a string t * is called a longest common subsequence of the strings in S. Note that a string t is called a subsequence of a string s, if t can be produced from s by deleting characters. For example, dga is a subsequence of adagtta. Due to its classical nature, the LCS problem has attracted quite a lot of research efforts over the past decades. Much work has been dedicated to the development of efficient dynamic programming procedures (see, for example, [2]). The body of work on approximate methods is dominated by constructive one-pass heuristics [9], [10]. Moreover, metaheuristics have been proposed in [17], [8], [12]. In [3] we recently published the current state-of-the-art algorithm for the LCS problem. The results of this algorithm, which is based on beam search (BS), have shown that none of the earlier algorithms came even close to derive good solutions for difficult problem instances. In other words, BS has shown to be largely superior to any other existing technique for what concerns the LCS problem. In this work we try to improve over the state-of-the-art results of BS by adding a learning component to BS. The resulting algorithm is a hybrid between the metaheuristic ant colony optimization (ACO) [7] and beam search. The interested reader might note that we already published a preliminary version of this

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