Heuristics for the phylogeny problem

被引:15
作者
Andreatta, AA
Ribeiro, CC
机构
[1] Univ Rio de Janeiro, Dept Appl Comp Sci, BR-22270 Rio De Janeiro, Brazil
[2] Catholic Univ Rio de Janeiro, Dept Comp Sci, BR-22453900 Rio De Janeiro, Brazil
关键词
phylogeny problem; phylogenetic trees; evolutionary trees; parsimony; local search; heuristics; frameworks;
D O I
10.1023/A:1015439913121
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A phylogeny is a tree that relates taxonomic units, based on their similarity over a set of characters. The problem of finding a phylogeny with the minimum number of evolutionary steps (the so-called parsimony criterion) is one of the main problems in comparative biology. In this work, we study different heuristic approaches to the phylogeny problem under the parsimony criterion. New algorithms based on metaheuristics are also proposed. All heuristics are implemented and compared under the same framework, leading to consistent and thorough comparative results. Computational results are reported for benchmark instances from the literature.
引用
收藏
页码:429 / 447
页数:19
相关论文
共 30 条
[1]  
ANDREATTA AA, 2002, OPTIMIZATION SOFTWAR, P60
[2]  
ANDREATTA AA, 1998, THESIS CATHOLIC U RI
[3]  
[Anonymous], 1996, Pattern-Oriented Software Architecture: A System of Patterns
[4]   THE MYTH OF EVE - MOLECULAR-BIOLOGY AND HUMAN ORIGINS [J].
AYALA, FJ .
SCIENCE, 1995, 270 (5244) :1930-1936
[5]  
BODLAENDER H, 1992, P 19 INT C AUT LANG, P273
[6]   THE COMPUTATIONAL-COMPLEXITY OF INFERRING ROOTED PHYLOGENIES BY PARSIMONY [J].
DAY, WHE ;
JOHNSON, DS ;
SANKOFF, D .
MATHEMATICAL BIOSCIENCES, 1986, 81 (01) :33-42
[7]   PARSIMONIOUS PHYLOGENETIC TREES IN METRIC-SPACES AND SIMULATED ANNEALING [J].
DRESS, A ;
KRUGER, M .
ADVANCES IN APPLIED MATHEMATICS, 1987, 8 (01) :8-37
[8]   METHODS FOR COMPUTING WAGNER TREES [J].
FARRIS, JS .
SYSTEMATIC ZOOLOGY, 1970, 19 (01) :83-&
[9]   Object-oriented application frameworks [J].
Fayad, ME ;
Schmidt, DC .
COMMUNICATIONS OF THE ACM, 1997, 40 (10) :32-38
[10]   GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES [J].
FEO, TA ;
RESENDE, MGC .
JOURNAL OF GLOBAL OPTIMIZATION, 1995, 6 (02) :109-133