Computer science and parsimony: a reappraisal, with discussion of methods for poorly structured datasets
In recent years, several publications in computer science journals have proposed new heuristic methods for parsimony analysis. This contribution discusses those papers, including methods highly praised by their authors, such as Hydra, Sampars and GA + PR + LS. Trees of comparable or better scores ca...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/12477 |
| Acceso en línea: | http://hdl.handle.net/11336/12477 |
| Access Level: | acceso abierto |
| Palabra clave: | Parsimony Phylogeny Cladistics https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
| Sumario: | In recent years, several publications in computer science journals have proposed new heuristic methods for parsimony analysis. This contribution discusses those papers, including methods highly praised by their authors, such as Hydra, Sampars and GA + PR + LS. Trees of comparable or better scores can be obtained using the program TNT, but from one to three orders of magnitude faster. In some cases, the search methods are very similar to others long in use in phylogenetics, but the enormous speed differences seem to correspond more to poor implementations than to actual differences in the methods themselves. |
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