A Genetic Algorithm Formulation for Rogue Taxa Problem

Srivastava, Abhishek; Jha, Balanand; Fahad, Md. Shah; Deepak, A.; Abhishek, Kumar

doi:10.1109/bsb.2018.8770676

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2021

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“…Alternatively, the relatively strong performance of H1 demonstrates the promise of computationally simpler heuristic approaches. Further improvements may be possible by modifying aspects of this approach, for example by using other quantifications of leaf stability (e.g., Estabrook 1992 ; Thorley and Wilkinson 1999 ; Pol and Escapa 2009 ; Goloboff and Szumik 2015 ), or by integrating other heuristic methods, such as a “genetic” evolutionary approach ( Srivastava et al 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Using Information Theory to Detect Rogue Taxa and Improve Consensus Trees

Smith

2021

Systematic Biology

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‘Rogue’ taxa of uncertain affinity can confound attempts to summarize the results of phylogenetic analyses. Rogues reduce resolution and support values in consensus trees, potentially obscuring strong evidence for relationships between other taxa. Information theory provides a principled means of assessing the congruence between a set of trees and their consensus, allowing rogue taxa to be identified more effectively than when using ad hoc measures of tree quality. A basic implementation of this approach in R recovers reduced consensus trees that are better resolved, more accurate, and more informative than those generated by existing methods.

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Section: Discussionmentioning

confidence: 99%

Using Information Theory to Detect Rogue Taxa and Improve Consensus Trees

Smith

2021

Systematic Biology

View full text Add to dashboard Cite

show abstract

A Genetic Algorithm Formulation for Rogue Taxa Problem

Cited by 1 publication

References 17 publications

Using Information Theory to Detect Rogue Taxa and Improve Consensus Trees

Using Information Theory to Detect Rogue Taxa and Improve Consensus Trees

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