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A LinearTime Algorithm for Computing Inversion Distance between Signed Permutations with an Experimental Study
 Journal of Computational Biology
, 2001
"... Hannenhalli and Pevzner gave the first polynomialtime algorithm for computing the inversion distance between two signed permutations, as part of the larger task of determining the shortest sequence of inversions needed to transform one permutation into the other. Their algorithm (restricted to dist ..."
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Cited by 116 (14 self)
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Hannenhalli and Pevzner gave the first polynomialtime algorithm for computing the inversion distance between two signed permutations, as part of the larger task of determining the shortest sequence of inversions needed to transform one permutation into the other. Their algorithm (restricted to distance calculation) proceeds in two stages: in the first stage, the overlap graph induced by the permutation is decomposed into connected components; then, in the second stage, certain graph structures (hurdles and others) are identified. Berman and Hannenhalli avoided the explicit computation of the overlap graph and gave an O(n alpha(n)) algorithm, based on a UnionFind structure, to find its connected components, where a is the inverse Ackerman function. Since for all practical purposes alpha(n) is a constant no larger than four, this algorithm has been the fastest practical algorithm to date. In this paper, we present a new lineartime algorithm for computing the connected components, which is more efficient than that of Berman and Hannenhalli in both theory and practice. Our algorithm uses only a stack and is very easy to implement. We give the results of computational experiments over a large range of permutation pairs produced through simulated evolution; our experiments show a speedup by a factor of 2 to 5 in the computation of the connected components and by a factor of 1.3 to 2 in the overall distance computation.
The accuracy of fast phylogenetic methods for large datasets
 In Proc. 7th Pacific Symp. on Biocomputing (PSB02
, 2002
"... Wholegenome phylogenetic studies require various sources of phylogenetic signals to produce an accurate picture of the evolutionary history of a group of genomes. In particular, sequencebased reconstruction will play an important role, especially in resolving more recent events. But using sequence ..."
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Cited by 9 (4 self)
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Wholegenome phylogenetic studies require various sources of phylogenetic signals to produce an accurate picture of the evolutionary history of a group of genomes. In particular, sequencebased reconstruction will play an important role, especially in resolving more recent events. But using sequences at the level of whole genomes means working with very large amounts of data—large numbers of sequences—as well as large phylogenetic distances, so that reconstruction methods must be both fast and robust as well as accurate. We study the accuracy, convergence rate, and speed of several fast reconstruction methods: neighborjoining, Weighbor (a weighted version of neighborjoining), greedy parsimony, and a new phylogenetic reconstruction method based on diskcovering and parsimony search (DCMNJ+MP). Our study uses extensive simulations based on random birthdeath trees, with controlled deviations from ultrametricity. We find that Weighbor, thanks to its sophisticated handling of probabilities, outperforms other methods for short sequences, while our new method is the best choice for sequence lengths above 100. For very large sequence lengths, all four methods have similar accuracy, so that the speed of neighborjoining and greedy parsimony makes them the two methods of choice. 1
Reconstructing optimal phylogenetic trees: a challenge in experimental algorithmics
 Experimental Algorithmics, volume 2547 of Lecture Notes in Computer Science
, 2002
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DOI: 10.1109/ICDAR.2011.18 An Open Architecture for EndtoEnd Document Analysis Benchmarking
, 2011
"... Abstract—In this paper, we present a fully operational, scalable and open architecture allowing endtoend document analysis benchmarking without needing to develop the whole pipeline. By decomposing the analysis process into coarsegrained tasks, and by building upon community provided stateofthe a ..."
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Abstract—In this paper, we present a fully operational, scalable and open architecture allowing endtoend document analysis benchmarking without needing to develop the whole pipeline. By decomposing the analysis process into coarsegrained tasks, and by building upon community provided stateofthe art algorithms, our architecture allows any combination of elementary document analysis algorithms, regardless their running system environment, programming language or data structures. Its flexible structure makes it straightforward to plug in new algorithms, compare them to other algorithms, and observe the effects on endtoend tasks without need to install, compile or otherwise interact with any other software than one’s own. Keywordsbenchmark; web services; document analysis; performance evaluation; I.
Pacific Symposium on Biocomputing 7:211222 (2002) The Accuracy of Fast Phylogenetic Methods for Large Datasets
"... Wholegenome phylogenetic studies require various sources of phylogenetic signals to produce an accurate picture of the evolutionary history of a group of genomes. In particular, sequencebased reconstruction will play an important role, especially in resolving more recent events. But using sequence ..."
Abstract
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Wholegenome phylogenetic studies require various sources of phylogenetic signals to produce an accurate picture of the evolutionary history of a group of genomes. In particular, sequencebased reconstruction will play an important role, especially in resolving more recent events. But using sequences at the level of whole genomes means working with very large amounts of data—large numbers of sequences—as well as large phylogenetic distances, so that reconstruction methods must be both fast and robust as well as accurate. We study the accuracy, convergence rate, and speed of several fast reconstruction methods: neighborjoining, Weighbor (a weighted version of neighborjoining), greedy parsimony, and a new phylogenetic reconstruction method based on diskcovering and parsimony search (DCMNJ+MP). Our study uses extensive simulations based on random birthdeath trees, with controlled deviations from ultrametricity. We find that Weighbor, thanks to its sophisticated handling of probabilities, outperforms other methods for short sequences, while our new method is the best choice for sequence lengths above 100. For very large sequence lengths, all four methods have similar accuracy, so that the speed of neighborjoining and greedy parsimony makes them the two methods of choice. 1
Experimental Analysis of Optimization Algorithms: Tuning and Beyond
"... Abstract This chapter comprises the essence of several years of tutorials the authors gave on experimental research in evolutionary computation. We highlight the renaissance of experimental techniques also in other fields to especially focus on the specific conditions of experimental research in com ..."
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Abstract This chapter comprises the essence of several years of tutorials the authors gave on experimental research in evolutionary computation. We highlight the renaissance of experimental techniques also in other fields to especially focus on the specific conditions of experimental research in computer science, or more concrete, metaheuristic optimization. The experimental setup is discussed together with the pitfalls awaiting the unexperienced (and sometimes even the experienced). We present a severity criterion as a metastatistical concept for evaluating statistical inferences, which can be used to avoid fallacies, i.e., misconceptions resulting from incorrect reasoning in argumentation caused by floor or ceiling effects. The sequential parameter optimization is discussed as a metastatistical framework which integrates concepts such as severity. Parameter tuning is considered as a relatively new tool in method design and analysis, and it leads to the question of adaptability of optimization algorithms. Another branch of experimentation aims for attaining more concrete problem knowledge, we may term it ‘exploratory landscape analysis’, containing sample and visualization techniques that are often applied but not seen as being a methodological contribution. However, this chapter is not only a renarration of well known facts. We also try a look into the future to estimate what the hot topics of methodological research will be in the next years and what changes we may expect for the whole community.