Clustering based distributed phylogenetic tree construction

Phylogenetic tree construction has received much attention recently due to the availability of vast biological data. In this study, we provide a three step method to build phylogenetic trees. Firstly, a density-based clustering algorithm is used to provide clusters of the population at hand using the distance matrix which shows the distances of the species. Secondly, a phylogenetic tree for each cluster is constructed by using the neighbor-joining (NJ) algorithm and finally, the roots of the small phylogenetic trees are connected again by the NJ algorithm to form one large phylogenetic tree. To our knowledge, this is the first method for building phylogenetic trees that uses clustering prior to forming the tree. As such, it provides independent phylogenetic tree formation within each cluster as the second step, hence is suitable for parallel/distributed processing, enabling fast processing of very large biological data sets.The proposed method, clustered neighbor-joining (CNJ) is applied to 145 samples from the Y-DNA Haplogroup G. Distances between male samples are the variation in their set of Y-chromosomal short tandem repeat (STR) values. We show that the clustering method we use is superior to other clustering methods as applied to Y-DNA data and also independent, fast distributed construction of phylogenetic trees is possible with this method.     

Multi-objective evolutionary algorithms and phylogenetic inference with multiple data sets

Evolutionary relationships among species are usually (1) illustrated by means of a phylogenetic tree and (2) inferred by optimising some measure of fitness, such as the total evolutionary distance between species or the likelihood of the tree (given a model of the evolutionary process and a data set). The combinatorial complexity of inferring the topology of the best tree makes phylogenetic inference an ideal candidate for evolutionary algorithms. However, difficulties arise when different data sets provide conflicting information about the inferred `best' tree(s). We apply the techniques of multi-objective optimisation to phylogenetic inference for the first time. We use the simplest model of evolution and a four species problem to illustrate the method.     

SAWSA-LPR: Astochastic search strategy for estimation of maximum likelihood DNA phylogenetic trees

In the spirit of the “grand challenge”, this paper covers the development of novel concepts for inference of large phylogenies based on the maximum likelihood method, which has proved to be the most accurate model for inference of huge and complex phylogenetic trees. Here, a novel method called Leaf Pruning and Re-grafting (LPR) has being presented, which is a variant of standard Sub-tree Pruning and Re-grafting (SPR) technique. LPR is a systematic approach where only unique topologies are generated at each step. Various stochastic search strategies for estimation of the maximum likelihood (ML) tree have also being proposed. Here, simulated annealing has been combined with steepest accent method to improve the quality of the final tree obtained. All the current simulated annealing approaches are used with simple hill climbing method to avoid the large number of repeated topologies that are normally generated by SPR. This easily leads to local maxima. However in the present study steepest accent with simulated annealing by way of LPR (SAWSA-LPR) has being used; the chances of returning local maxima has being significantly reduced. A straightforward and efficient parallel version of simulated annealing with steepest accent to accelerate the process of DNA phylogenetic tree inference has also being presented. It was observed that the implementation of the algorithm based on random DNA sequences gave better results as compared to other tree construction methods.     

Parallel Formulations of Decision-Tree Classification Algorithms

Classification decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classification decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classification decision trees have a natural concurrency, but are difficult to parallelize due to the inherent dynamic nature of the computation. In this paper, we present parallel formulations of classification decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. We also provide the analysis of the cost of computation and communication of the proposed hybrid method. Moreover, experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.     

Hybrid multiobjective artificial bee colony for multiple sequence alignment

In the bioinformatics community, it is really important to find an accurate and simultaneous alignment among diverse biological sequences which are assumed to have an evolutionary relationship. From the alignment, the sequences homology is inferred and the shared evolutionary origins among the sequences are extracted by using phylogenetic analysis. This problem is known as the multiple sequence alignment (MSA) problem. In the literature, several approaches have been proposed to solve the MSA problem, such as progressive alignments methods, consistency-based algorithms, or genetic algorithms (GAs). In this work, we propose a Hybrid Multiobjective Evolutionary Algorithm based on the behaviour of honey bees for solving the MSA problem, the hybrid multiobjective artificial bee colony (HMOABC) algorithm. HMOABC considers two objective functions with the aim of preserving the quality and consistency of the alignment: the weighted sum-of-pairs function with affine gap penalties (WSP) and the number of totally conserved (TC) columns score. In order to assess the accuracy of HMOABC, we have used the BAliBASE benchmark (version 3.0), which according to the developers presents more challenging test cases representing the real problems encountered when aligning large sets of complex sequences. Our multiobjective approach has been compared with 13 well-known methods in bioinformatics field and with other 6 evolutionary algorithms published in the literature.     

Parallel sphere detector algorithm providing optimal MIMO detection on massively parallel architectures

Multiple‐input multiple‐output (MIMO) systems have attracted considerable attention in wireless communications because they offer a significant increase in data throughput and link coverage without additional bandwidth requirement or increased transmit power. The price that has to be paid is the increased complexity of hardware components and algorithms. The sphere detector (SD) algorithm solves the problem of maximum likelihood (ML) detection for MIMO channels by significantly reducing the search space of possible solutions. The main drawback of the SD algorithm is in its sequential nature, consequently, running it on massively parallel architectures (MPAs) is very inefficient. In order to overcome the drawbacks of the SD algorithm, a new parallel sphere detector (PSD) algorithm is proposed. It implements a novel hybrid tree search method, where the algorithm parallelism is assured by the efficient combination of depth‐first search and breadth‐first search algorithms. A path metric‐based parallel sorting is employed at each intermediate stage. The PSD algorithm is able to adjust its memory requirements and extent of parallelism to fit a wide range of parallel architectures. Mapping details for MPAs are proposed by giving the details of thread dependent, highly parallel building blocks of the algorithm. Based on the building blocks proposed, a mapping to general‐purpose graphics processing unit is provided, and its performance is evaluated. In order to achieve high‐throughput, several levels of parallelism are introduced, and different scheduling strategies are considered. Copyright © 2015 John Wiley & Sons, Ltd.     

A new multiobjective clustering technique based on the concepts of stability and symmetry

Most clustering algorithms operate by optimizing (either implicitly or explicitly) a single measure of cluster solution quality. Such methods may perform well on some data sets but lack robustness with respect to variations in cluster shape, proximity, evenness and so forth. In this paper, we have proposed a multiobjective clustering technique which optimizes simultaneously two objectives, one reflecting the total cluster symmetry and the other reflecting the stability of the obtained partitions over different bootstrap samples of the data set. The proposed algorithm uses a recently developed simulated annealing-based multiobjective optimization technique, named AMOSA, as the underlying optimization strategy. Here, points are assigned to different clusters based on a newly defined point symmetry-based distance rather than the Euclidean distance. Results on several artificial and real-life data sets in comparison with another multiobjective clustering technique, MOCK, three single objective genetic algorithm-based automatic clustering techniques, VGAPS clustering, GCUK clustering and HNGA clustering, and several hybrid methods of determining the appropriate number of clusters from data sets show that the proposed technique is well suited to detect automatically the appropriate number of clusters as well as the appropriate partitioning from data sets having point symmetric clusters. The performance of AMOSA as the underlying optimization technique in the proposed clustering algorithm is also compared with PESA-II, another evolutionary multiobjective optimization technique.     

Constructing large suffix trees on a computational grid

The suffix tree is a key data structure for biological sequence analysis, since it permits efficient solutions to many string-based problems. Constructing large suffix trees is challenging because of high memory overheads and poor memory locality. Even though efficient suffix tree construction algorithms exist, their run-time is still very high for long DNA sequences such as whole human chromosomes. In this paper, we are using a hierarchical grid system as a computational platform in order to reduce this run-time significantly. To achieve an efficient mapping onto this type of architecture we introduce a parallel suffix tree construction algorithm that makes use of a new data structure called the common prefix suffix tree. Using this algorithm together with a dynamic load balancing strategy we show that our distributed grid implementation leads to significant run-time savings.     

Derivatives of tree sets with applications to grammatical inference

Tree automata generalize the notion of a finite automaton working on strings to that of a finite automaton operating on trees. Most results for finite automata have been extended to tree automata. In this paper we introduce tree derivatives which extend the concept of Brzozowski's string derivatives. We use tree derivatives for minimizing and characterizing tree automata. Tree derivatives are used as a basis of inference of tree automata from finite samples of trees. Our method compares tree derivative sets and infers a tree automaton based on the amount of overlap between the derivative sets. Several of the limitations present in the tree inference techniques by Brayer and Fu and Edwards, Gonzalez, and Thomason are not imposed by our algorithm.     

Reducing correlation of random forest–based learning‐to‐rank algorithms using subsample size

Learning‐to‐rank (LtR) has become an integral part of modern ranking systems. In this field, the random forest–based rank‐learning algorithms are shown to be among of the top performers. Traditionally, each tree of a random forest is learnt using a bootstrapped copy of the training set, where approximately 63% of the examples are unique. The goal of using a bootstrapped copy instead of the original training set is to reduce the correlation between individual trees, thereby making the prediction of the ensemble more accurate. In this regard, the following question may be raised: how can we leverage the correlation between the trees in favor of performance and scalability of a random forest–based LtR algorithm? In this article, we investigate whether we can further decrease the correlation between the trees while maintaining or possibly improving accuracy. Among several potential options to achieve this goal, we investigate the size of the subsamples used for learning individual trees. We examine the performance of a random forest–based LtR algorithm as we control the correlation using this parameter. Experiments on LtR data sets reveal that for small‐ to moderate‐sized data sets, substantial reduction in training time can be achieved using only a small amount of training data per tree. Moreover, due to the positive correlation between the variability across the trees and performance of a random forest, we observe an increase in accuracy while maintaining the same level of model stability as the baseline. For big data sets, although our experiments did not observe an increase in accuracy (because, with larger data sets, the individual tree variance is already comparatively smaller), our technique is still applicable as it allows for greater scalability.     

Matrix Trees

We propose a new data representation for octrees and kd‐trees that improves upon memory size and algorithm speed of existing techniques. While pointerless approaches exploit the regular structure of the tree to facilitate efficient data access, their memory footprint becomes prohibitively large as the height of the tree increases. Pointerbased trees require memory consumption proportional to the number of tree nodes, thus exploiting the typical sparsity of large trees. Yet, their traversal is slowed by the need to follow explicit pointers across the different levels. Our solution is a pointerless approach that represents each tree level with its own matrix, as opposed to traditional pointerless trees that use only a single vector. This novel data organization allows us to fully exploit the tree's regular structure and improve the performance of tree operations. By using a sparse matrix data structure we obtain a representation that is suited for sparse and dense trees alike. In particular, it uses less total memory than pointer‐based trees even when the data set is extremely sparse. We show how our approach is easily implemented on the GPU and illustrate its performance in typical visualization scenarios.     

Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning

Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions.     

Efficient parallel branch-and-bound algorithm for constructing minimum ultrametric trees

The construction of evolutionary trees is important for computational biology, especially for the development of biological taxonomies. The ultrametric tree (UT) is a commonly used model for evolutionary trees assuming that the rate of evolution is constant (molecular clock hypothesis). However, the construction of minimum ultrametric trees (MUTs, principle of minimum evolution) has been shown to be NP-hard even from a metric distance matrix. The branch-and-bound algorithm is generally used to solve a wide variety of NP-hard problems. In previous work, a sequential branch-and-bound algorithm for constructing MUTs (BBU) was presented and the experimental results showed that it is useful for MUT construction. Hence, in this study, an efficient parallel branch-and-bound algorithm (PBBU) for constructing MUTs or near-MUTs from a metric distance matrix was designed. A random data set as well as some practical data sets of Human + Chimpanzee Mitochondrial and Bacteriophage T7 DNAs were used to test the PBBU. The experimental results show that the PBBU found an optimal solution for 36 species on 16 PCs within a reasonable time. To the best of our knowledge, no algorithm has been found to solve this problem even for 25 species. Moreover, the PBBU achieved satisfying speed-up ratios for most of the test cases.     

A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing

The supply trajectory of electric power for submerged arc magnesia furnace determines the yields and grade of magnesia grain during the manufacture process. As the two production targets (i.e., the yields and the grade of magnesia grain) are conflicting and the process is subject to changing conditions, the supply of electric power needs to be dynamically optimized to track the moving Pareto optimal set with time. A hybrid evolutionary multiobjective optimization strategy is proposed to address the dynamic multiobjective optimization problem. The hybrid strategy is based on two techniques. The first one uses case-based reasoning to immediately generate good solutions to adjust the power supply once the environment changes, and then apply a multiobjective evolutionary algorithm to accurately solve the problem. The second one is to learn the case solutions to guide and promote the search of the evolutionary algorithm, and the best solutions found by the evolutionary algorithm can be used to update the case library to improve the accuracy of case-based reasoning in the following process. Due to the effectiveness of mutual promotion, the hybrid strategy can continuously adapt and search in dynamic environments. Two prominent multiobjective evolutionary algorithms are integrated into the hybrid strategy to solve the dynamic multiobjective power supply optimization problem. The results from a series of experiments show that the proposed hybrid algorithms perform better than their component multiobjective evolutionary algorithms for the tested problems.     

Analysis of DNA sequence transformations on grids

Study of the evolution of species or organisms is essential for various biological applications. Evolution is typically studied at the molecular level by analyzing the mutations of DNA sequences of organisms. Techniques have been developed for building phylogenetic or evolutionary trees for a set of sequences. Though phylogenetic trees capture the overall evolutionary relationships among the sequences, they do not reveal fine-level details of the evolution. In this work, we attempt to resolve various fine-level sequence transformation details associated with a phylogenetic tree using cellular automata. In particular, our work tries to determine the cellular automata rules for neighbor-dependent mutations of segments of DNA sequences. We also determine the number of time steps needed for evolution of a progeny from an ancestor and the unknown segments of the intermediate sequences in the phylogenetic tree. Due to the existence of vast number of cellular automata rules, we have developed a grid system that performs parallel guided explorations of the rules on grid resources. We demonstrate our techniques by conducting experiments on a grid comprising machines in three countries and obtaining potentially useful statistics regarding evolutions in three HIV sequences. In particular, our work is able to verify the phenomenon of neighbor-dependent mutations and find that certain combinations of neighbor-dependent mutations, defined by a cellular automata rule, occur with greater than 90% probability. We also find the average number of time steps for mutations for some branches of phylogenetic tree over a large number of possible transformations with standard deviations less than 2.     

An application of an OR-Parallel Prolog system to phylogenetic analysis

In this paper we discuss an application of our OR-Parallel Prolog system to a search problem of importance in practice: the construction of phylogenetic trees. The use of a maximum likelihood method to construct such trees is based on concrete models of evolutional processes and is well-motivated statistically. However, the use of maximum likelihood methods has been hindered by the computational cost to calculate the likelihood of possible alternative trees (i.e. their confidence scores) at each search step for the optimal tree. To cope with this problem, we used OR-parallel Prolog as a coordination language to orchestrate the search for the optimal tree. A numerical computation written in C computed the likelihood of each alternative tree and a symbolic computation written in Prolog performed a parallel A* tree search. For constructing phylogenetic trees of bacterial organisms, our parallel algorithm allowed us to increase the size of the search space, allowing us to include nearly optimal as well as optimal intermediate trees in the search. Our priority mechanism reduced the generation of useless tasks and resulted in superlinear speedups in some cases.     

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

This paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO‐GVNS) metaheuristic, named adapted MO‐GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO‐GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA‐II), and NSGA‐III, and another MO‐GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem.     

Drawing non‐layered tidy trees in linear time

The well‐known Reingold–Tilford algorithm produces tidy‐layered drawings of trees: drawings where all nodes at the same depth are vertically aligned. However, when nodes have varying heights, layered drawing may use more vertical space than necessary. A non‐layered drawing of a tree places children at a fixed distance from the parent, thereby giving a more vertically compact drawing. Moreover, non‐layered drawings can also be used to draw trees where the vertical position of each node is given, by adding dummy nodes. In this paper, we present the first linear‐time algorithm for producing non‐layered drawings. Our algorithm is a modification of the Reingold–Tilford algorithm, but the original complexity proof of the Reingold–Tilford algorithm uses an invariant that does not hold for the non‐layered case. We give an alternative proof of the algorithm and its extension to non‐layered drawings. To improve drawings of trees of unbounded degree, extensions to the Reingold–Tilford algorithm have been proposed. These extensions also work in the non‐layered case, but we show that they then cause a O(n²) run‐time. We then propose a modification to these extensions that restores the O(n) run‐time. Copyright © 2013 John Wiley & Sons, Ltd.     

基于自适应免疫遗传算法的智能组卷

对多目标组合优化的组卷问题,借鉴生物免疫系统原理中抗体多样性产生及保持机理,定义多目标选择熵和浓度调节选择概率概念,利用自适应免疫遗传算法,运用抗体克隆、高变异策略,实现组卷问题的多目标优化。该算法充分体现了pareto最优解的概念,具有并行搜索及个体编码长度动态调整、pareto最优个体保存于群体外(免疫记忆)并不断更新等特点。     

The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms

This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean–variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2), and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets.