Better Hill-Climbing Searches for Parsimony
نویسندگان
چکیده
The reconstruction of evolutionary trees is a major problem in biology, and many evolutionary trees are estimated using heuristics for the NP-hard optimization problem Maximum Parsimony. The current heuristics for searching through tree space use a particular technique, called “tree-bisection and reconnection”, or TBR, to transform one tree into another tree; other less-frequently used transformations, such as SPR and NNI, are special cases of TBR. In this paper, we describe a new tree-rearrangement operation which we call the p-ECR move, for p-Edge-Contract-and-Refine. Our results include an efficient algorithm for computing the best 2-ECR neighbors of a given tree, based upon a simple data structure which also allows us to efficiently calculate the best neighbors under NNI, SPR, and TBR operations (as well as efficiently running the greedy sequence addition technique for maximum parsimony). More significantly, we show that the 2-ECR neighborhood of a given tree is incomparable to the neighborhood defined by TBR, and properly contains all trees within two NNI moves. Hence, the use of the 2-ECR move, in conjunction with TBR and/or NNI moves, may be a more effective technique for exploring tree space than TBR alone.
منابع مشابه
Characterizing Local Optima for Maximum Parsimony.
Finding the best phylogenetic tree under the maximum parsimony optimality criterion is computationally difficult. We quantify the occurrence of such optima for well-behaved sets of data. When nearest neighbor interchange operations are used, multiple local optima can occur even for "perfect" sequence data, which results in hill-climbing searches that never reach a global optimum. In contrast, w...
متن کاملComparison of Genetic and Hill Climbing Algorithms to Improve an Artificial Neural Networks Model for Water Consumption Prediction
No unique method has been so far specified for determining the number of neurons in hidden layers of Multi-Layer Perceptron (MLP) neural networks used for prediction. The present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. The data used in the present research for prediction are consumption data of water...
متن کاملJournal of Classification 11:209-232 (1994) A Tree 9 A Window 9 A Hill; Generalization of Nearest- Neighbor Interchange in Phylogenetic Optimization
The method of nearest-neighbor interchange effects local improvements in a binary tree by replacing a 4-subtree by one of its two alternatives if this improves the objective function. We extend this to k-subtrees to reduce the number of local optima. Possible sequences of k-subtrees to be examined are produced by moving a window over the tree, incorporating one edge at a time while deactivating...
متن کاملFinding Accurate Fro A Knowledge-Intensive
An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accurac...
متن کاملSearch Heuristics, Case-based Reasoning And Software Project Effort Prediction
This paper reports on the use of search techniques to help optimise a case-based reasoning (CBR) system for predicting software project effort. A major problem, common to ML techniques in general, has been dealing with large numbers of case features, some of which can hinder the prediction process. Unfortunately searching for the optimal feature subset is a combinatorial problem and therefore N...
متن کامل