A massively parallel genetic algorithm for RNA secondary structure prediction

We present a new method for predicting RNA secondary structure based on a genetic algorithm. The algorithm is designed to run on a massively parallel SIMD computer. Statistical analysis shows that the program performs well when compared to a dynamic programming algorithm used to solve the same problem. The program has also pointed out a long-standing simplification in the implementation of the original dynamic programming algorithm that sometimes causes it not to find the optimal secondary structure.     

基于伪并行遗传算法的聚类分析方法

针对传统的基于聚类准则的聚类算法初始化敏感和容易陷入局部极值的问题,设计了一种新的基于伪并行遗传算法的聚类方法.采用实数编码方式对每个样本所属的类别进行编码,通过空类的识别和修复来修正不舍法的染色体.在引入离散随机变异算子和优化方向变异算子的基础上,结合迁移策略和插入策略,达到兼顾局部收敛速度和全局收敛性能的目的.与K-均值算法对比仿真实验,表明了这种基于伪并行遗传算法的聚类新方法的可行性和有效性.     

Graphics of RNA secondary structure; towards an object-oriented algorithm

We present a new algorithm for the display of RNA secondary structure. The principle of the algorithm is entirely different from those currently in use in that our algorithm is 'object oriented' while current algorithms are 'procedural'. The circular RNA molecule of chrysanthemum stunt viroid was used as input data for demonstrating the operation of the program. The major interest of this method will be found in its potential use in simulation graphics of RNA folding processes.     

Experiences with a parallel algorithm for data flow analysis

We have designed a family of parallel data flow analysis algorithms for execution on distributed-memory MIMD machines, based on general-purpose, hybrid algorithms for data flow analysis [Marlowe and Ryder 1990]. We exploit a natural partitioning of the hybrid algorithms and explore a static mapping, dynamic scheduling strategy. Alternative mapping-scheduling choices and refinements of the flow graph condensation used are discussed. Our parallel hybrid algorithm family is illustrated on Reaching Definitions, although parallel algorithms also exist for many interprocedural (e.g., Aliasing) and intraprocedural (e.g., Available Expressions) problems [Marlowe 1989]. We have implemented the parallel hybrid algorithm for Reaching Definitions on an Intel iPSC/2. Our empirical results suggest the practicality of parallel hybrid algorithms.An earlier version of this paper was presented at Supercomputing '90.The research reported here was supported, in part, by the New Jersey Commission on Science and Technology and the CAIP Center's Industrial Members, by Siemens Research Corporation and by National Science Foundation grant CCR-8920078.     

A bayesian statistical algorithm for RNA secondary structure prediction.

A Bayesian approach for predicting RNA secondary structure that addresses the following three open issues is described: (1) the need for a representation of the full ensemble of probable structures; (2) the need to specify a fixed set of energy parameters; (3) the desire to make statistical inferences on all variables in the problem. It has recently been shown that Bayesian inference can be employed to relax or eliminate the need to specify the parameters of bioinformatics recursive algorithms and to give a statistical representation of the full ensemble of probable solutions with the incorporation of uncertainty in parameter values. In this paper, we make an initial exploration of these potential advantages of the Bayesian approach. We present a Bayesian algorithm that is based on stacking energy rules but relaxes the need to specify the parameters. The algorithm returns the exact posterior distribution of the number of destabilizing loops, stacking energy matrices, and secondary structures. The algorithm generates statistically representative structures from the full ensemble of probable secondary structures in exact proportion to the posterior probabilities. Once the forward recursions for the algorithm are completed, the backward recursive sampling executes in O(n) time, providing a very efficient approach for generating representative structures. We demonstrate the utility of the Bayesian approach with several tRNA sequences. The potential of the approach for predicting RNA secondary structures and presenting alternative structures is illustrated with applications to the Escherichia coli tRNA(Ala) sequence and the Xenopus laevis oocyte 5S rRNA sequence.     

High-dimensional quantum dynamics of molecules on surfaces: a massively parallel implementation

We present a massively parallel implementation to perform quantum dynamical wave packet calculations of molecules on surfaces. The employed algorithm propagates the wavefunction via the time-dependent Schrödinger equation within a finite basis representation by Split and Chebyshev schemes, respectively. For the parallelization, a problem adapted data decomposition in all dimensions is introduced that ensures an optimal load balancing. In a speedup analysis of the timing and scaling properties, the overall semi-linear scaling of the algorithm is verified. The almost linear speedup up to 512 processing elements indicates our implementation as a powerful tool for high-dimensional calculations. The implementation is applied to laser induced desorption of molecules from surfaces.     

Performance analysis of the CM-2, a massively parallel SIMD computer

The performance evaluation process for a massively parallel distributed-memory SIMD computer is described generally. The performance in basic computation, grid communication, and computation with grid communication is analysed. A practical performance evaluation and analysis study is done for the Connection Machine 2, and conclusions about its performance are drawn.     

Statistical exploratory analysis of genetic algorithms

Genetic algorithms have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters, and how performance varies with respect to changes in parameters. This paper presents a rigorous yet practical statistical methodology for the exploratory study of genetic and other adaptive algorithms. This methodology addresses the issues of experimental design, blocking, power calculations, and response curve analysis. It details how statistical analysis may assist the investigator along the exploratory pathway. As a demonstration of our methodology, we describe case studies using four well-known test functions. We find that the effect upon performance of crossover is pre-dominantly linear, while the effect of mutation is predominantly quadratic. Higher order effects are noted but contribute less to overall behavior. In the case of crossover, both positive and negative gradients are found suggesting the use of a maximum crossover rate for some problems and its exclusion for others. For mutation, optimal rates appear higher compared with earlier recommendations in the literature, while supporting more recent work. The significance of interaction and the best values for crossover and mutation are problem specific.     

6-DOF并联机器人控制算法结构化分析与设计

为了实现对六自由度并联机器人位置速度算法的控制,针时控制算法采用了软件工程的原理和方法进行了分析,并对机器人运动特性进行了研究.根据运动特性对位置与速度的控制算法进行了解析,提出位置反解的控制策略,给出了功能分析的DFD模型和软件结构化设计模型SC图.描述了算法的详细设计及实现,采用结构化语言表达工具,并对实现中重要的部分细节进行了细致的研究,其结果达到了位置速度控制的目的及机器人控制算法的软件结构化设计.     

Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem

The test task scheduling problem (TTSP) has attracted increasing attention due to the wide range of automatic test systems applications, despite the fact that it is an NP-complete problem. The main feature of TTSP is the close interactions between task sequence and the scheme choice. Based on this point, the parallel implantation of genetic algorithm, called Parallel Genetic Algorithm (PGA), is proposed to determine the optimal solutions. Two branches—the tasks sequence and scheme choice run the classic genetic algorithm independently and they balance each other due to their interaction in the given problem. To match the frame of the PGA, a vector group encoding method is provided. In addition, the fitness distance coefficient (FDC) is first applied as the measurable step of landscape to analyze TTSP and guide the design of PGA when solving the TTSP. The FDC is the director of the search space of the TTSP, and the search space determinates the performance of PGA. The FDC analysis shows that the TTSP owes a large number of local optima. Strong space search ability is needed to solve TTSP better. To make PGA more suitable to solve TTSP, three crossover and four selection operations are adopted to find the best combination. The experiments show that due to the characteristic of TTSP and the randomness of the algorithm, the PGA has a low probability for optimizing the TTSP, but PGA with Nabel crossover and stochastic tournament selection performs best. The assumptions of FDC are consistent with the success rate of PGA when solving the TTSP.     

Efficient distributed mesh data structure for parallel automated adaptive analysis

For the purpose of efficiently supporting parallel mesh-based simulations, we developed a partition model and a distributed mesh data management system that is able to shape its mesh data structure dynamically based on the user’s representational needs to provide the needed representation at a minimum cost (memory and time), called Flexible distributed Mesh DataBase (FMDB). The purpose of the partition model is to represent mesh partitioning and support mesh-level parallel operations through inter-processor communication links. FMDB has been used to efficiently support parallel automated adaptive analysis processes in conjunction with existing analysis engines.     

Haplotype estimation from genotypical data by genetic algorithm

The study of a disease using genetic identification has become possible by using haplotype information. The expectation-maximization algorithms are the standard approach in haplotype analysis. These approaches maximize the likelihood function of a genotypic distribution assuming Hardy-Weinberg equilibrium. However, these methods are time-consuming when applied to the sequence of many loci. In this study, we used a genetic algorithm to obtain the haplotype frequencies from the frequencies of genotypes. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Optimizing the distribution of shopping centers with parallel genetic algorithm

This study optimizes the distribution of shopping centers (SCs) to satisfy vendors and shoppers in terms of realizing the shortest car-based shopping trips in an urban area. First, modal split between car and transit is estimated, and then the interaction between land-use and transportation in the context of choice of shopping destinations is modeled to build an optimal function. Second, parallel genetic algorithm (PGA) is used to solve the optimal problem and several problems in application of GA are discussed. Finally, a case study which examines the effectiveness of the method, is carried out.     

基于模糊关联规则并行挖掘算法的飞行数据处理

针对顺序的模糊关联规则算法在处理海量飞行数据时,由于算法可扩展性低、响应时间过长而带来数据处理的不便,本文采用模糊关联并行挖掘算法,先使用并行的模糊c-2均值算法将数量型属性划分成若干个模糊集,并借助模糊集软化属性的划分边界:在用改进的布尔型关联规则的并行挖掘算法来发现频繁模糊属性集.通过飞行数据库进行算法验证,证明了并行算法具有好的可扩展性、规模增长性和加速比性能.     

Elucidating the conceptual structure of a business domain via exploratory network analysis of business survey data

Traditional hypothesis-driven research domains such as molecular biology are undergoing paradigm shift in becoming progressively data-driven, enabling rapid acquisition of new knowledge. The purpose of this article is to promote an analogous development in business research. Specifically, we focus on network analysis: given the key constructs in a business research domain, we introduce a data-driven protocol applicable to business survey data to (a) discover the web of influence directionalities among the key constructs and therein identify the critical constructs, and to (b) determine the relative contributions of the constructs in predicting the levels of the critical constructs. In (a), we build a directed connectivity graph by (i) using a state of the art statistical technique to perform variable selection, (ii) integrating the variable selection results to form the directed connectivity graph, and (iii) employing graph-theoretical concepts and a graph clustering technique to interpret the resulting network topology in a multi-resolution manner. In (b), based on the directed connectivity graph, multiple linear regression is performed to quantify relations between the critical and other constructs. As a case study, the protocol is applied to analyze opinion leading and seeking behaviors in online market communications environments. The directed connectivity relations revealed provide new ways of visualizing the web of influence directionalities among the constructs of interest, suggest new research directions to pursue, and aid decision making in marketing management. The proposed method provides a data-driven alternative to traditional confirmatory methods in analyzing relations among given constructs. Its flexibility enables the business researcher to broaden the scope of research he/she can fruitfully engage in.     

Solving the forward kinematics of parallel manipulators with a genetic algorithm

A floating point genetic algorithm is proposed to solve the forward kinematic problem for parallel manipulators. This method, adapted from studies in the biological sciences, allows the use of inverse kinematic solutions to solve forward kinematics as an optimization problem. The method is applied to two 3-degree-of-freedom planar parallel manipulators and to a 3-degree-of-freedom spherical manipulator. The method converges to a solution within a broader search domain compared to a Newton-Raphson scheme. © 1996 John Wiley & Sons, Inc.     

A network parallel genetic algorithm for the one machine sequencing problem

This paper presents a network parallel genetic algorithm for the one machine sequencing problem. It examines a parallel genetic algorithm in which processors exchange their best solution found at periodic intervals and the case when no exchange is performed. The network parallel genetic algorithm is executed on a cluster of IBM RS/6000 workstations using a master-slave approach. Performance to a serial genetic algorithm is reported.     

A parallel algorithm for molecular dynamics simulation of branched molecules

To get an insight into the effects of molecular architecture in the behaviour of thin lubricant films we have devised an algorithm for simulation of branched molecules. We have used this algorithm successfully to simulate branched isomers of C₃₀. However the algorithm is flexible enough to be used for the simulation of more complex branched molecules. The resulting algorithm can be used in molecular dynamics simulation of branched molecules and could be helpful in designing new materials at the molecular level.     

Integrated data structure and scheduling approach for manufacturing and transportation using hybrid genetic algorithm

The scheduling systems in industries are required to construct schedule considering many kind of elements. The Advanced Planning and Scheduling (APS) is an approach for combined problems. To realize APS system, it is important to integrate data structure and scheduling algorithm using these data. In this paper, we propose integrated data structure based on Bill of Manufacturing with information technology of XML family and new Multistage Operation-based Genetic Algorithm for scheduling subsystem. The results of numerical experiment validate effectiveness of the proposal methods. Received: June 2005/Accepted: December 2005