The construction of rapid algorithms for constructing molecular phylogenetic trees based on the principles of metaheuristic algorithms

构建分子系统发育树的算法主要有两种。第一种是基于距离矩阵的二叉树，另一种是在给定目标函数的最小化或最大化条件下寻找最优二叉树。最大简约法、最大似然法和最小均方法是后一种算法的典型例子。虽然在第二种情况下，在巨大的可行解空间中寻找最优解是一个棘手的问题，但他们从未意识到如何克服解空间的巨大策略的重要性，也从未研究过局部搜索的方法。结果，他们忽略了除了在贪婪选择的初始解附近找到更好解的可能性。我们在这里断言，所谓的甲基启发式算法在上述局部搜索过程中确实工作得很好。实际上，我们已经从模拟中证明，禁忌搜索，一种著名的方法启发式算法，极大地扩展了局部搜索路径的遍历。

There are two main types of algorithms for constructing molecular phylogenetic trees. The first is those based on the distance matrix, while the other tries to search for an optimal binary tree under minimizing or maximizing given objective functions. The Maximum Parsimony, the Maximum-Liklihood, and the Minimum mean square are the typical examples of the latter Algorithms.Although it is an intractable problem to find an optimal solution in the huge feasible solution space in the second cases, they have not ever realized the importance of the strategy for how to overcome the hugeness of the solution spaces, and have never investigated the methods of local searches. As a result, they neglect the possibility of finding better solutions other than those in the neighborhood of the greedily chosen initial solution.We assert here that the so-called methaheuristic algorithms do work well in the above procedures of local searches. And actually, we have shown from the simulations that Tabu search, one of the well-known methaheuristic algorithms, greatly contribute the expand the traverse of the local search trails.