NAPX: A polynomial time approximation scheme for the Noah's ark problem

Glenn Hickey, Paz Carmi, Anil Maheshwari, Norbert Zeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The Noah's Ark Problem (NAP) is an NP-Hard optimization problem with relevance to ecological conservation management. It asks to maximize the phylogenetic diversity (PD) of a set of taxa given a fixed budget, where each taxon is associated with a cost of conservation and a probability of extinction. NAP has received renewed interest with the rise in availability of genetic sequence data, allowing PD to be used as a practical measure of biodiversity. However, only simplified instances of the problem, where one or more parameters are fixed as constants, have as of yet been addressed in the literature. We present NAPX, the first algorithm for the general version of NAP that returns a 1∈-∈ε approximation of the optimal solution. It runs in time where n is the number of species, and B is the total budget and h is the height of the input tree. We also provide improved bounds for its expected running time.

Original languageEnglish
Title of host publicationAlgorithms in Bioinformatics - 8th International Workshop, WABI 2008, Proceedings
Pages76-86
Number of pages11
DOIs
StatePublished - 28 Nov 2008
Externally publishedYes
Event8th International Workshop on Algorithms in Bioinformatics, WABI 2008 - Karlsruhe, Germany
Duration: 15 Sep 200819 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5251 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Workshop on Algorithms in Bioinformatics, WABI 2008
Country/TerritoryGermany
CityKarlsruhe
Period15/09/0819/09/08

Keywords

  • Approximation algorithm
  • Noah's ark problem
  • Phylogenetic diversity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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