Quicke D L, Taylor J, Purvis A
Unit of Parasite Systematics, CABI, Bioscience UK Centre (Ascot), Department of Biology, Imperial College, Silwood Park, Ascot, Berkshire, SL5 7PY.
Syst Biol. 2001 Feb;50(1):60-6.
In this paper we describe a new heuristic strategy designed to find optimal (parsimonious) trees for data sets with large numbers of taxa and characters. This new strategy uses an iterative searching process of branch swapping with equally weighted characters, followed by swapping with reweighted characters. This process increases the efficiency of the search because, after each round of swapping with reweighted characters, the subsequent swapping with equal weights will start from a different group (island) of trees that are only slightly, if at all, less optimal. In contrast, conventional heuristic searching with constant equal weighting can become trapped on islands of suboptimal trees. We test the new strategy against a conventional strategy and a modified conventional strategy and show that, within a given time, the new strategy finds trees that are markedly more parsimonious. We also compare our new strategy with a recent, independently developed strategy known as the Parsimony Ratchet.
在本文中,我们描述了一种新的启发式策略,旨在为具有大量分类单元和性状的数据集找到最优(简约)树。这种新策略使用了一个迭代搜索过程,先对权重相等的性状进行分支交换,然后对重新加权的性状进行交换。这个过程提高了搜索效率,因为在每一轮对重新加权的性状进行交换之后,随后对权重相等的性状进行交换将从一组不同的(次优程度)仅略低(如果有的话)的树(“岛屿”)开始。相比之下,采用恒定相等权重的传统启发式搜索可能会陷入次优树的“岛屿”中。我们将新策略与传统策略和改进的传统策略进行了测试,结果表明,在给定时间内,新策略找到的树明显更简约。我们还将我们的新策略与最近独立开发的一种称为简约棘轮的策略进行了比较。