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Computing Distances between Evolutionary Trees

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Book cover Handbook of Combinatorial Optimization

Abstract

Comparing objects to find their similarities or, equivalently, dissimilarities, is a fundamental issue in many fields including pattern recognition, image analysis, drug design, the study of thermodynamic costs of computing, cognitive science, etc. Various models have been introduced to measure the degree of similarity or dissimilarity in the literature. In the latter case the degree of dissimilarity is also often referred to as the distance. While some distances are straightforward to compute, e.g. the Hamming distance for binary strings, the Euclidean distance for geometric objects; some others are formulated as combinatorial optimization problems and thus pose nontrivial challenging algorithmic problems, sometimes even uncomputable, such as the universal information distance between two objects [4].

Supported in part by a CGAT (Canadian Genome Analysis and Technology) grant. Work done while the author was at McMaster University and University of Waterloo.

Supported in part by NSF grant 9205982 and CGAT.

Supported in part by NSERC Operating Grant OGP0046613 and CGAT.

Supported in part by the NSERC Operating Grant OGP0046506 and CGAT.

Supported in part by CGAT and NSERC Internation Fellowship.

Supported in part by Hong Kong Research Council.

Supported in part by CGAT.

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Author information

Authors and Affiliations

  1. Rutgers University, USA

    Bhaskar DasGupta

  2. SUNY at Buffalo, USA

    Xin He

  3. McMaster University, Canada

    Tao Jiang

  4. City University of Hong Kong and University of Waterloo, China

    Ming Li

  5. CWI, USA

    John Tromp

  6. City University of Hong Kong, China

    Lusheng Wang

  7. National University of Singapore, Singapore

    Louxin Zhang

Authors
  1. Bhaskar DasGupta
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  2. Xin He
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  3. Tao Jiang
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  4. Ming Li
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  5. John Tromp
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  6. Lusheng Wang
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  7. Louxin Zhang
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Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, USA

    Ding-Zhu Du

  2. University of Florida, Gainesville, USA

    Panos M. Pardalos

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© 1998 Kluwer Academic Publishers

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DasGupta, B. et al. (1998). Computing Distances between Evolutionary Trees. In: Du, DZ., Pardalos, P.M. (eds) Handbook of Combinatorial Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0303-9_11

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  • DOI: https://doi.org/10.1007/978-1-4613-0303-9_11

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7987-4

  • Online ISBN: 978-1-4613-0303-9

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