Multi-edition approach for Median String Problem

Abreu, José Ignacio; Mirabal, Pedro

doi:10.1109/sccc51225.2020.9281222

Cited by 1 publication

(2 citation statements)

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“…Experiments show that the algorithm is faster than [10]; however, they provide no details on the quality of the solution. Later the above strategy was revisited in [45], where they show that there may be an inverse relationship between convergence speed and solution quality since as the number of simultaneous operations increases, the quality of the solution deteriorates. The authors also stated that this could slightly increase the probability of finding the global optimum since algorithms that change only one symbol at a time can get stuck at a local optimum.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…Most algorithm start from the empty string [1], [5] or from the string belonging to the dataset that accumulates the least distance to the rest, understood as the median of the dataset [1], [4], [10], [14], [29], [43]- [45]. Our goal is to propose an initialization alternative since, from the works above, initialization affects the speed of convergence of the algorithms.…”

Section: Our Proposalmentioning

confidence: 99%

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Boosting Perturbation-Based Iterative Algorithms to Compute the Median String

et al. 2021

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The most competitive heuristics for calculating the median string are those that use perturbation-based iterative algorithms. Given the complexity of this problem, which under many formulations is NP-hard, the computational cost involved in the exact solution is not affordable. In this work, the heuristic algorithms that solve this problem are addressed, emphasizing its initialization and the policy to order possible editing operations. Both factors have a significant weight in the solution of this problem. Initial string selection influences the algorithm's speed of convergence, as does the criterion chosen to select the modification to be made in each iteration of the algorithm. To obtain the initial string, we use the median of a subset of the original dataset; to obtain this subset, we employ the Half Space Proximal (HSP) test to the median of the dataset. This test provides sufficient diversity within the members of the subset while at the same time fulfilling the centrality criterion. Similarly, we provide an analysis of the stop condition of the algorithm, improving its performance without substantially damaging the quality of the solution. To analyze the results of our experiments, we computed the execution time of each proposed modification of the algorithms, the number of computed editing distances, and the quality of the solution obtained. With these experiments, we empirically validated our proposal.

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Section: Preliminaries and Related Workmentioning

confidence: 99%