Dimension Of Search Space Research Articles

An empirical evaluation of a three‐tier conduit framework for multifaceted test case classification and selection using fuzzy‐ant colony optimisation approach

SummaryThe test case optimisation is an NP‐complete, knowledge‐driven, data‐driven, and multidimensional search space partitioning and dimension reduction problem. In the multifaceted test case classification, partitioning and reducing the multidimensional test case fitness search space is the critical problem. The vague nature of fitness parameters, conflicting nature objectives, and ambiguity in the test case fitness evaluation have created and increased the uncertainty, the imprecision, and the incompleteness in the test case classification and selection. Because of the increasing ambiguity, the complexity, and the cost of software testing, automated test case classification and selection has emerged as an appropriate tool to classify test cases into predefined categories using the multifaceted concept. Most of the test cases affecting the performance of the classifier are irrelevant and redundant. A strong need therefore exists to devise an intelligent technique to identify and remove test cases affecting the performance of the classifier. For increasing the performance of classifier, multifaceted test case selection is used to reduce fitness search space to be searched. In this paper, a three‐tier sequential framework is proposed for a multifaceted test case classification and selection. The first stage of the proposed framework is the fuzzy synthesis‐based filtration approach for multifaceted test case fitness evaluation and classification. The second stage of the proposed framework is the fuzzy entropy‐based filtration technique with a backward search strategy, used for estimating and reducing the ambiguity in test case fitness evaluation, classification, and selection. The third stage of the proposed framework is the ant colony optimisation‐based wrapper technique with a forward search strategy, employed to select test cases from the output (reduced) test suite by the second stage. The proposed framework is tested on artefacts of benchmark applications. The results of the empirical study clearly show that the third stage of our proposed method outperforms the second and first stages, and the performance of the algorithms used in all three stages increases on average as the stages are escalating. The classification accuracy is enhanced by reducing the ambiguity in fitness and the classification of test cases, increasing the number of test cases accurately classified, and reducing the number in the test case pool to be exercised. Copyright © 2014 John Wiley & Sons, Ltd.

A unified graphical models framework for automated mitosis detection in human embryos.

Time lapse microscopy has emerged as an important modality for studying human embryo development, as mitosis events can provide insight into embryo health and fate. Mitosis detection can happen through tracking of embryonic cells (tracking based), or from low level image features and classifiers (tracking free). Tracking based approaches are challenged by high dimensional search space, weak features, outliers, missing data, multiple deformable targets, and weak motion model. Tracking free approaches are data driven and complement tracking based approaches. We pose mitosis detection as augmented simultaneous segmentation and classification in a conditional random field (CRF) framework that combines both approaches. It uses a rich set of discriminative features and their spatiotemporal context. It performs a dual pass approximate inference that addresses the high dimensionality of tracking and combines results from both components. For 312 clinical sequences we measured division events to within 30 min and observed an improvement of 25.6% and a 32.9% improvement over purely tracking based and tracking free approach respectively, and close to an order of magnitude over a traditional particle filter. While our work was motivated by human embryo development, it can be extended to other detection problems in image sequences of evolving cell populations.

Server Allocation Approach for P2P Networks and its Heuristic Algorithm

PDF HTML阅读 XML下载 导出引用 引用提醒 P2P网络服务器部署方案及其启发式优化算法 DOI: 10.3724/SP.J.1001.2013.04362 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(60970146);国家高技术研究发展计划(863)(2007AA01Z449) Server Allocation Approach for P2P Networks and its Heuristic Algorithm Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为了更好地利用服务器提升P2P 网络的性能,提出了一种P2P 网络服务器最优化部署方案.该方案将如何部署有限的服务器资源来满足尽量多节点的问题,转化为一个带有约束条件的最优化问题.考虑到极坐标空间适合于表达长度相关的约束条件,例如部署方案中的约束条件,提出一种基于极坐标映射的启发式算法,解决P2P 网络服务器最优部署问题.相比于通常的使用罚函数的启发式算法,该算法可以有效地减小搜索空间,其性能的改善在搜索空间维数高的情况下更加显著.实验结果表明,所提出的服务器部署方案可以满足更多节点的需求,能够有效提高P2P 网络的性能. Abstract:An optimal allocation approach of servers for improving the performance of P2P networks is proposed in this paper. The approach is to allocate a limited number of servers over the P2P networks to satisfy as many peers as possible, by using a constrained optimization algorithm. In considering that the polar space is more suitable to express length-related constraints, such as those in this approach, a heuristic algorithm based on the polar coordinator mapping is proposed to deal with the optimization problem of the server allocation. Compared with the existing heuristic algorithms that are based on penalty functions, this algorithm can effectively reduce the search space, and the performance is better especially in a high dimensional search space. The experiment results show that our server allocation approach can satisfy more peers' requirement and effectively improve the performance of P2P networks. 参考文献 相似文献 引证文献

Evolving Agent-based Models Using Complexification Approach

This paper focuses on parameter search for multi-agent based models using evolutionary algorithms. Large numbers and variable dimensions of parameters require a search method which can efficiently handle a high dimensional search space. We are proposing the use of complexification as it emulates the natural way of evolution by starting with a small constrained search space and expanding it as the evolution progresses. We examined the effects of this method on an EA by evolving parameters for two multi-agent based models.

Finite inflation of a hyperelastic toroidal membrane over a cylindrical rim

The present paper is devoted to the study of finite inflation of a hyperelastic toroidal membrane on a cylindrical rim under uniform internal pressure. Both compliant and rigid frictionless rims have been considered. The compliant cylindrical rim is modeled as a linear distributed stiffness. The initial cross-section of the torus is assumed to be circular, and the membrane material is assumed to be a homogeneous and isotropic Mooney–Rivlin solid. The problem is formulated as a two point boundary value problem and solved using a shooting method by employing the Nelder–Meads search technique. The optimization function is constructed on a two (three) dimensional search space for the compliant cylinder (rigid cylinder). The effect of the inflation pressure, material properties and elastic properties of the rim on the state of stretch and stress, and on the geometry of the inflated torus have been studied, and some interesting results have been obtained. The stability of the inflated configurations in terms of occurrence of the impending wrinkling state in the membrane has also been studied.

Phased searching with NEAT in a Time-Scaled Framework: Experiments on a computer-aided detection system for lung nodules

In the field of computer-aided detection (CAD) systems for lung nodules in computed tomography (CT) scans, many image features are presented and many artificial neural network (ANN) classifiers with various structural topologies are analyzed; frequently, the classifier topologies are selected by trial-and-error experiments. To avoid these trial and error approaches, we present a novel classifier that evolves ANNs using genetic algorithms, called "Phased Searching with NEAT in a Time or Generation-Scaled Framework", integrating feature selection with the classification task. We analyzed our method's performance on 360 CT scans from the public Lung Image Database Consortium database. We compare our method's performance with other more-established classifiers, namely regular NEAT, Feature-Deselective NEAT (FD-NEAT), fixed-topology ANNs, and support vector machines (SVMs) using ten-fold cross-validation experiments of all 360 scans. The results show that the proposed "Phased Searching" method performs better and faster than regular NEAT, better than FD-NEAT, and achieves sensitivities at 3 and 4 false positives (FP) per scan that are comparable with the fixed-topology ANN and SVM classifiers, but with fewer input features. It achieves a detection sensitivity of 83.0±9.7% with an average of 4FP/scan, for nodules with a diameter greater than or equal to 3mm. It also evolves networks with shorter evolution times and with lower complexities than regular NEAT (p=0.026 and p<0.001, respectively). Analysis on the average and best network complexities evolved by regular NEAT and by our approach shows that our approach searches for good solutions in lower dimensional search spaces, and evolves networks without superfluous structure. We have presented a novel approach that combines feature selection with the evolution of ANN topology and weights. Compared with the original threshold-based Phased Searching method of Green, our method requires fewer parameters and converges to the optimal network complexity required for the classification task at hand. The results of the ten-fold cross-validation experiments also show that our proposed CAD system for lung nodule detection performs well with respect to other methods in the literature.

Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization

Evolutionary computation (EC) paradigm has undergone extensions in the recent years diverging from the natural process of genetic evolution to the simulation of natural life processes exhibited by the living organisms. Bee colonies exemplify a high level of intrinsic interdependence and co-ordination among its members, and algorithms inspired from the bee colonies have gained recent prominence in the field of swarm based metaheuristics. The artificial bee colony (ABC) algorithm was recently developed, by simulating the minimalistic foraging model of honeybees in search of food sources, for solving real-parameter, non-convex, and non-smooth optimization problems. The single parameter perturbation in classical ABC resulted in fairly commendable performance for simple problems without epistasis of variables (separable). However, it suffered from narrow search zone and slow convergence which eventually led to poor exploitation tendency. Even with the increase in dimensionality, a significant deterioration was observed in the ability of ABC to locate the optimum in a huge search volume. Some of the probable shortcomings in the basic ABC approach, as observed, are the single parameter perturbation instead of a multiple one, ignoring the fitness to reward ratio while selecting food sites, and most importantly the absence of environmental factors in the algorithm design. Research has shown that spatial environmental factors play a crucial role in insect locomotion and foragers seem to learn the direction to be undertaken based on the relative analysis of its proximal surroundings. Most importantly, the mapping of the forager locomotion from three dimensional search spaces to a multidimensional solution space calls forth the implementation of multiple modification schemes. Based on the fundamental observation pertaining to the dynamics of ABC, this article proposes an improved variant of ABC aimed at improving the optimizing ability of the algorithm over an extended set of problems. The hybridization of the proposed fitness learning mechanism with a weighted selection scheme and proximity based stimuli helps to achieve a fine blending of explorative and exploitative behaviour by enhancing both local and global searching ability of the algorithm. This enhances the ability of the swarm agents to detect optimal regions in the unexplored fitness basins. With respect to its immediate surroundings, a proximity based component is added to the normal positional modification of the onlookers and is enacted through an improved probability selection scheme that takes the T/E (total reward to distance) ratio metric into account. The biologically-motivated, hybridized variant of ABC achieves a statistically superior performance on majority of the tested benchmark instances, as compared to some of the most prominent state-of-the-art algorithms, as is demonstrated through a detailed experimental evaluation and verified statistically.

MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

The prediction and analysis of the three- dimensional (3D) structure of proteins is a key research problem in Structural Bioinformatics. The 1990's Genome Projects resulted in a large increase in the number of available protein sequences. However, the number of identified 3D protein structures have not followed the same growth trend. Currently, the number of available protein sequences greatly exceeds the number of known 3D structures. Many computational methodologies, systems and algorithms have been proposed to address the protein structure prediction problem. However, the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. The most significant progress in the last Critical Assessment of protein Structure Prediction was achieved by methods that use database information. Nevertheless, a major challenge remains in the development of better strategies for template identification and representation. This article describes a computational strategy to acquire and represent structural information of experimentally determined 3D protein structures. A clustering strategy was combined with artificial neural networks in order to extract structural information from experimental protein structure templates. In the proposed strategy, the main efforts focus on the acquisition of useful and accurate structural information from 3D protein templates stored in the Protein Data Bank (PDB). The proposed method was tested in twenty protein sequences whose sizes vary from 14 to 70 amino acid residues. Our results show that the proposed method is a good way to extract and represent valuable information obtained from the PDB and also significantly reduce the 3D protein conformational search space.

Integer-grid maps for reliable quad meshing

Quadrilateral remeshing approaches based on global parametrization enable many desirable mesh properties. Two of the most important ones are (1) high regularity due to explicit control over irregular vertices and (2) smooth distribution of distortion achieved by convex variational formulations. Apart from these strengths, state-of-the-art techniques suffer from limited reliability on real-world input data, i.e. the determined map might have degeneracies like (local) non-injectivities and consequently often cannot be used directly to generate a quadrilateral mesh. In this paper we propose a novel convex Mixed-Integer Quadratic Programming (MIQP) formulation which ensures by construction that the resulting map is within the class of so called Integer-Grid Maps that are guaranteed to imply a quad mesh. In order to overcome the NP-hardness of MIQP and to be able to remesh typical input geometries in acceptable time we propose two additional problem specific optimizations: a complexity reduction algorithm and singularity separating conditions. While the former decouples the dimension of the MIQP search space from the input complexity of the triangle mesh and thus is able to dramatically speed up the computation without inducing inaccuracies, the latter improves the continuous relaxation, which is crucial for the success of modern MIQP optimizers. Our experiments show that the reliability of the resulting algorithm does not only annihilate the main drawback of parametrization based quad-remeshing but moreover enables the global search for high-quality coarse quad layouts - a difficult task solely tackled by greedy methodologies before.

Electrostatic-Consistent Coarse-Grained Potentials for Molecular Simulations of Proteins.

We present a new generation of coarse-grained (CG) potentials that account for a simplified electrostatic description of soluble proteins. The treatment of permanent electrostatic dipoles of the backbone and polar side-chains allows to simulate proteins, preserving an excellent structural and dynamic agreement with respective reference structures and all-atom molecular dynamics simulations. Moreover, multiprotein complexes can be well described maintaining their molecular interfaces thanks to the ability of this scheme to better describe the actual electrostatics at a CG level of resolution. An efficient and robust heuristic algorithm based on particle swarm optimization is used for the derivation of CG parameters via a force-matching procedure. The ability of this protocol to deal with high dimensional search spaces suggests that the extension of this optimization procedure to larger data sets may lead to the generation of a fully transferable CG force field. At the present stage, these electrostatic-consistent CG potentials are easily and efficiently parametrized, show a good degree of transferability, and can be used to simulate soluble proteins or, more interestingly, large macromolecular assemblies for which long all-atom simulations may not be easily affordable.

The Wind Driven Optimization Technique and its Application in Electromagnetics

A new type of nature-inspired global optimization methodology based on atmospheric motion is introduced. The proposed Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the potential to implement constraints on the search domain. At its core, a population of infinitesimally small air parcels navigates over an $N$ -dimensional search space following Newton's second law of motion, which is also used to describe the motion of air parcels within the earth's atmosphere. Compared to similar particle based algorithms, WDO employs additional terms in the velocity update equation (e.g., gravitation and Coriolis forces), providing robustness and extra degrees of freedom to fine tune. Along with the theory and terminology of WDO, a numerical study for tuning the WDO parameters is presented. WDO is further applied to three electromagnetics optimization problems, including the synthesis of a linear antenna array, a double-sided artificial magnetic conductor for WiFi applications, and an E-shaped microstrip patch antenna. These examples suggest that WDO can, in some cases, out-perform other well-known techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) or Differential Evolution (DE) and that WDO is well-suited for problems with both discrete and continuous-valued parameters.

EVO—Evolutionary algorithm for crystal structure prediction

We present EVO—an evolution strategy designed for crystal structure search and prediction. The concept and main features of biological evolution such as creation of diversity and survival of the fittest have been transferred to crystal structure prediction.EVO successfully demonstrates its applicability to find crystal structures of the elements of the 3rd main group with their different spacegroups. For this we used the number of atoms in the conventional cell and multiples of it. Running EVO with different numbers of carbon atoms per unit cell yields graphite as the lowest energy structure as well as a diamond-like structure, both in one run. Our implementation also supports the search for 2D structures and was able to find a boron sheet with structural features so far not considered in literature. Program summaryProgram title: EVOCatalogue identifier: AEOZ_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOZ_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License version 3No. of lines in distributed program, including test data, etc.: 23488No. of bytes in distributed program, including test data, etc.: 1830122Distribution format: tar.gzProgramming language: Python.Computer: No limitations known.Operating system: Linux.RAM: Negligible compared to the requirements of the electronic structure programs usedClassification: 7.8.External routines: Quantum ESPRESSO (http://www.quantum-espresso.org/), GULP (https://projects.ivec.org/gulp/)Nature of problem:Crystal structure search is a global optimisation problem in 3N+3 dimensions where N is the number of atoms in the unit cell. The high dimensional search space is accompanied by an unknown energy landscape.Solution method:Evolutionary algorithms transfer the main features of biological evolution to use them in global searches. The combination of the “survival of the fittest” (deterministic) and the randomised choice of the parents and normally distributed mutation steps (non-deterministic) provides a thorough search.Restrictions:The algorithm is in principle only restricted by a huge search space and simultaneously increasing calculation time (memory, etc.), which is not a problem for our piece of code but for the used electronic structure programs.Running time:The simplest provided case runs serially and takes 30 minutes to one hour. All other calculations run for significantly longer time depending on the parameters like the number and sort of atoms and the electronic structure program in use as well as the level of parallelism included.

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

We present FitSKIRT, a method to efficiently fit radiative transfer models to UV/optical images of dusty galaxies. These images have the advantage that they have better spatial resolution compared to FIR/submm data. FitSKIRT uses the GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo radiative transfer code. Genetic algorithms prove to be a valuable tool in handling the multi- dimensional search space as well as the noise induced by the random nature of the Monte Carlo radiative transfer code. FitSKIRT is tested on artificial images of a simulated edge-on spiral galaxy, where we gradually increase the number of fitted parameters. We find that we can recover all model parameters, even if all 11 model parameters are left unconstrained. Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral galaxy NGC4013. This galaxy has been modeled previously by other authors using different combinations of radiative transfer codes and optimization methods. Given the different models and techniques and the complexity and degeneracies in the parameter space, we find reasonable agreement between the different models. We conclude that the FitSKIRT method allows comparison between different models and geometries in a quantitative manner and minimizes the need of human intervention and biasing. The high level of automation makes it an ideal tool to use on larger sets of observed data.

On the Pareto-Optimal Beam Structure and Design for Multi-User MIMO Interference Channels

In this paper, the Pareto-optimal beam structure for multi-user multiple-input multiple-output (MIMO) interference channels is investigated and a necessary condition for any Pareto-optimal transmit signal covariance matrix is presented for the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$K$</tex></formula> -pair Gaussian MIMO interference channel. It is shown that any Pareto-optimal transmit signal covariance matrix at a transmitter should have its column space contained in the union of the signal spaces of the channel matrices from the transmitter to all receivers. Based on this necessary condition, an efficient parameterization for the beam search space is proposed. The proposed parameterization is given by the product manifold of a Stiefel manifold and a subset of a hyperplane and enables us to construct an efficient beam design algorithm by exploiting its rich geometrical structure and existing tools for optimization on Stiefel manifolds. Reduction in the beam search space dimension and computational complexity by the proposed parameterization and the proposed beam design approach is significant when the number of transmit antennas is much larger than the sum of the numbers of receive antennas, as in upcoming cellular networks adopting massive MIMO technologies. Numerical results validate the proposed parameterization and the proposed cooperative beam design method based on the proposed parameterization for MIMO interference channels.

Improving accuracy for cancer classification with a new algorithm for genes selection

BackgroundEven though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability.ResultsWe applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature.ConclusionsEvaluation of a gene’s contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered.

Dimensionality of consumer search space drives trophic interaction strengths

Trophic interactions govern biomass fluxes in ecosystems, and stability in food webs. Knowledge of how trophic interaction strengths are affected by differences among habitats is crucial for understanding variation in ecological systems. Here we show how substantial variation in consumption-rate data, and hence trophic interaction strengths, arises because consumers tend to encounter resources more frequently in three dimensions (3D) (for example, arboreal and pelagic zones) than two dimensions (2D) (for example, terrestrial and benthic zones). By combining new theory with extensive data (376 species, with body masses ranging from 5.24 × 10(-14) kg to 800 kg), we find that consumption rates scale sublinearly with consumer body mass (exponent of approximately 0.85) for 2D interactions, but superlinearly (exponent of approximately 1.06) for 3D interactions. These results contradict the currently widespread assumption of a single exponent (of approximately 0.75) in consumer-resource and food-web research. Further analysis of 2,929 consumer-resource interactions shows that dimensionality of consumer search space is probably a major driver of species coexistence, and the stability and abundance of populations.

A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides

Tertiary Protein Structure Prediction is one of the most important problems in Structural Bioinformatics. Along the last 20years many algorithms have been proposed as to solve this problem. However, it still remains a challenging issue because of the complexity and of the dimensionality of the protein conformational search space. In this article a first principle method which uses database information for the prediction of the 3-D structure of polypeptides is presented. The technique is based on the Group Method of Data Handling (GMDH) algorithm, implemented by a software tool introduced on this work. GMDH Polynomial Neural Networks have been used with success in many fields such as data mining, knowledge discovery, pattern recognition and prediction. The proposed method was tested with seven protein sequences whose sizes vary from 14 to 54 amino acid residues. Results show that the predicted tertiary structures adopt a fold similar to the experimental structures. RMSD and secondary structure analysis reveal that the proposed method present accurate results in their predictions. The predicted structures can be used as input structures in refinement methods based on molecular mechanics (MM), e.g. molecular dynamics (MD) simulations. The search space is expected to be greatly reduced and the ab initio methods can demand a much reduced computational time to achieve a more accurate polypeptide structure.

Tackling learning intractability through topological organization and regulation of cortical networks.

A key challenge in evolving control systems for robots using neural networks is training tractability. Evolving monolithic fixed topology neural networks is shown to be intractable with limited supervision in high dimensional search spaces. Common strategies to overcome this limitation are to provide more supervision by encouraging particular solution strategies, manually decomposing the task and segmenting the search space and network. These strategies require a supervisor with domain knowledge and may not be feasible for difficult tasks where novel concepts are required. The alternate strategy is to use self-organized task decomposition to solve difficult tasks with limited supervision. The artificial neural tissue (ANT) approach presented here uses self-organized task decomposition to solve tasks. ANT inspired by neurobiology combines standard neural networks with a novel wireless signaling scheme modeling chemical diffusion of neurotransmitters. These chemicals are used to dynamically activate and inhibit wired network of neurons using a coarse-coding framework. Using only a global fitness function that does not encourage a predefined solution, modular networks of neurons are shown to self-organize and perform task decomposition. This approach solves the sign-following task found to be intractable with conventional fixed and variable topology networks. In this paper, key attributes of the ANT architecture that perform self-organized task decomposition are shown. The architecture is robust and scalable to number of neurons, synaptic connections, and initialization parameters.

β-sheet Topology Prediction with High Precision and Recall for β and Mixed α/β Proteins

The prediction of the correct -sheet topology for pure and mixed proteins is a critical intermediate step toward the three dimensional protein structure prediction. The predicted beta sheet topology provides distance constraints between sequentially separated residues, which reduces the three dimensional search space for a protein structure prediction algorithm. Here, we present a novel mixed integer linear optimization based framework for the prediction of -sheet topology in and mixed proteins. The objective is to maximize the total strand-to-strand contact potential of the protein. A large number of physical constraints are applied to provide biologically meaningful topology results. The formulation permits the creation of a rank-ordered list of preferred -sheet arrangements. Finally, the generated topologies are re-ranked using a fully atomistic approach involving torsion angle dynamics and clustering. For a large, non-redundant data set of 2102 and mixed proteins with at least 3 strands taken from the PDB, the proposed approach provides the top 5 solutions with average precision and recall greater than 78%. Consistent results are obtained in the -sheet topology prediction for blind targets provided during the CASP8 and CASP9 experiments, as well as for actual and predicted secondary structures. The -sheet topology prediction algorithm, BeST, is available to the scientific community at http://selene.princeton.edu/BeST/.

Reduced Search Space Algorithm for Simultaneous Localization and Mapping in Mobile Robots

In this paper, we propose a new algorithm for simultaneous localization and mapping in mobile robots which uses evolutionary algorithm and particle swarm optimization. The proposed method is based on both local and global heuristic search methods. In each step of robot movements, the local search is applied in the small search space of odometry errors to improve the map accuracy. A global search method is applied for loop closing. The proposed algorithm detects loops and closes them, detects and solves correspondence and avoids local extremums. With a proper representation of problem parameters in chromosome, the dimensionality of search space is reduced. The proposed algorithm utilizes occupancy grid and does not require land marks which are not available in most natural environments. A new fitness function is proposed that is computationally efficient and eliminates the need for complex statistical calculations as used in current approaches. Results of experiments on real datasets exhibit the superior performance of the proposed method compared to the current methods. DOI: http://dx.doi.org/10.11591/ijra.v1i1.274 Full Text: PDF