Title :
An Approximation Algorithm for the Noah´s Ark Problem with Random Feature Loss
Author :
Hickey, Glenn ; Blanchette, Mathieu ; Carmi, Paz ; Maheshwari, Anil ; Zeh, Norbert
Author_Institution :
Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada
Abstract :
The phylogenetic diversity (PD) of a set of species is a measure of their evolutionary distinctness based on a phylogenetic tree. PD is increasingly being adopted as an index of biodiversity in ecological conservation projects. The Noah´s Ark Problem (NAP) is an NP-Hard optimization problem that abstracts a fundamental conservation challenge in asking to maximize the expected 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. Only simplified instances of the problem, where one or more parameters are fixed as constants, have as of yet been addressed in the literature. Furthermore, it has been argued that PD is not an appropriate metric for models that allow information to be lost along paths in the tree. We therefore generalize the NAP to incorporate a proposed model of feature loss according to an exponential distribution and term this problem NAP with Loss (NAPL). In this paper, we present a pseudopolynomial time approximation scheme for NAPL.
Keywords :
biology computing; computational complexity; ecology; evolution (biological); genetics; random processes; trees (mathematics); NAPL; NP-Hard optimization problem; Noah´s Ark problem; approximation algorithm; biodiversity index; ecological conservation projects; evolutionary distinctness; phylogenetic diversity; phylogenetic tree; pseudopolynomial time approximation scheme; random feature loss; taxon; Abstracts; Approximation algorithms; Biodiversity; Computer science; Cost function; Ecosystems; Exponential distribution; Loss measurement; Phylogeny; Resource management; Noah´s ark problem; approximation algorithm.; phylogenetic diversity; Algorithms; Biodiversity; Computational Biology; Phylogeny;
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
DOI :
10.1109/TCBB.2010.37