{"title":"用于系统发育重建的多目标人工蜂群实现性能分析","authors":"Sergio Santander-Jiménez, M. A. Vega-Rodríguez","doi":"10.1109/NaBIC.2014.6921850","DOIUrl":null,"url":null,"abstract":"The inference of phylogenetic relationships represents one of the most challenging problems in bioinformatics. The increasing availability of biological data motivates the development of new algorithmic designs to conduct phylogenetic analyses on exponentially increasing search spaces. Bioinspired metaheuristics have arisen as a useful approach to address this problem, introducing different search strategies according to the way phylogenetic trees are represented and handled by the algorithm. In this work, we study the multiobjective and biological performance achieved by different Multiobjective Artificial Bee Colony implementations based on direct (tree-based) and indirect (distance-based) individual representations. Experiments on four real nucleotide data sets show meaningful differences in multiobjective performance between the analyzed approaches, obtaining significant biological results in comparison with other state-of-the-art phylogenetic methods.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Performance analysis of Multiobjective Artificial Bee Colony implementations for phylogenetic reconstruction\",\"authors\":\"Sergio Santander-Jiménez, M. A. Vega-Rodríguez\",\"doi\":\"10.1109/NaBIC.2014.6921850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The inference of phylogenetic relationships represents one of the most challenging problems in bioinformatics. The increasing availability of biological data motivates the development of new algorithmic designs to conduct phylogenetic analyses on exponentially increasing search spaces. Bioinspired metaheuristics have arisen as a useful approach to address this problem, introducing different search strategies according to the way phylogenetic trees are represented and handled by the algorithm. In this work, we study the multiobjective and biological performance achieved by different Multiobjective Artificial Bee Colony implementations based on direct (tree-based) and indirect (distance-based) individual representations. Experiments on four real nucleotide data sets show meaningful differences in multiobjective performance between the analyzed approaches, obtaining significant biological results in comparison with other state-of-the-art phylogenetic methods.\",\"PeriodicalId\":209716,\"journal\":{\"name\":\"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NaBIC.2014.6921850\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NaBIC.2014.6921850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance analysis of Multiobjective Artificial Bee Colony implementations for phylogenetic reconstruction
The inference of phylogenetic relationships represents one of the most challenging problems in bioinformatics. The increasing availability of biological data motivates the development of new algorithmic designs to conduct phylogenetic analyses on exponentially increasing search spaces. Bioinspired metaheuristics have arisen as a useful approach to address this problem, introducing different search strategies according to the way phylogenetic trees are represented and handled by the algorithm. In this work, we study the multiobjective and biological performance achieved by different Multiobjective Artificial Bee Colony implementations based on direct (tree-based) and indirect (distance-based) individual representations. Experiments on four real nucleotide data sets show meaningful differences in multiobjective performance between the analyzed approaches, obtaining significant biological results in comparison with other state-of-the-art phylogenetic methods.