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Cloud platforms encompass a large number of storage services that can be used to manage the needs of customers. Each of these services, offered by a different provider, is characterized by specific features, limitations and prices. In presence of multiple options, it is crucial to select the best solution fitting the customer requirements in terms of quality of service and costs. Most of the available approaches are not able to handle uncertainty in the expression of subjective preferences from customers, and can result in wrong (or sub-optimal) service selections in presence of rational/selfish providers, exposing untrustworthy indications concerning the quality of service levels and prices associated to their offers. In addition, due to its multi-objective nature, the optimal service selection process results in a very complex task to be managed, when possible, in a distributed way, for well-known scalability reasons. In this work, we aim at facing the above challenges by proposing three novel contributions. The fuzzy sets theory is used to express vagueness in the subjective preferences of the customers. The service selection is resolved with the distributed application of fuzzy inference or Dempster-Shafer theory of evidence. The selection strategy is also complemented by the adoption of a game theoretic approach for promoting truth-telling ones among service providers. We present empirical evidence of the proposed solution effectiveness through properly crafted simulation experiments. [ABSTRACT FROM PUBLISHER]

In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management. [ABSTRACT FROM PUBLISHER]

In this paper, an ultrafast steady-state genetic algorithm processor (GAP) is presented. Due to the heavy computational load of genetic algorithms (GAs), they usually take a long time to find optimum solutions. Hardware implementation is a significant approach to overcome the problem by speeding up the GAs procedure. Hence, we designed a digital CMOS implementation of GA in 0.18~ \mu \textm process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously. [ABSTRACT FROM PUBLISHER]

This laboratory experiment provides undergraduate students enrolled in organic chemistry the opportunity to design and synthesize their own peptide, which is then tested for antimicrobial activity. After reading a primary scientific paper on antimicrobial peptides, students design and synthesize their own hexapeptide that they hypothesize will have antimicrobial activity. The students characterize their products by analyzing liquid chromatography-mass spectrometry and antimicrobial bioassay data for their synthesized peptide. The students are able to complete the synthesis and prepare their samples for analysis in three 34 h lab periods; instructors perform LC-MS and a bioassay with the peptides and provide data to students for analysis. This exercise is flexible and can be altered to include students performing the bioassay, or to meet different time constraints or target student populations. This experiment allows students to increase their knowledge of solid phase peptide chemistry and gain experience with developing and testing hypotheses through experimental design. [ABSTRACT FROM AUTHOR]

In this paper, an experimental apparatus for the analysis of biological cells light scattering in liquid suspensions has been presented. Firstly, an algorithm of the discrete sources method (DSM) is applied to model light scattering from a cell particle. In our model, a monochromatic laser beam scattered by cell particles, which can be inorganic, organic, or biological cells (animal cells and bacteria) and the scattering characteristic is analyzed with morphology and the refractive index of the cell particles. In contrast to traditional volume-based methods which are widely used for light scattering simulation, DSM allows calculation of scattering for all incident angles and polarizations at once. This leads to an essential reduction of the computing time. The DSM algorithm allows using a lower number of elementary sources which results in an increased accuracy of approximation for every harmonic. Secondly, in order to study light scattering in biological cells close to the actual situation, we focus on non-spherical particles in the cell-culture medium. Finally, we demonstrate the light scattering results of bovine kidney cells suspended in the cell-culture medium, and compares with the simulated results. [ABSTRACT FROM AUTHOR]

In matrix factorization problems, one seeks to decompose a data matrix into a product of two matrices—frequently, one captures meaningful information contained in the data, and the other specifies how this information is combined to generate the data matrix. In this paper, matrix factorization that arises in haplotype assembly, an important NP-hard problem in genomics, is studied. Haplotypes are sequences of chromosomal variations in an individual’s genome, which are of critical importance for understudying the individual’s susceptibility to various diseases. A novel formulation of haplotype assembly as the partially observed low-rank matrix factorization problem is proposed and efficiently solved via a modified gradient descent method that exploits salient structural properties of sequencing data. In particular, the observed matrix in the problem at hand contains noisy samples of the product of an informative matrix with rows having entries from a finite alphabet and a matrix with rows that are standard unit basis. Convergence of the proposed algorithm is analyzed and its performance tested on both synthetic and experimental data. The results demonstrate superior accuracy and speed of the proposed method as compared to state-of-the-art haplotype assembly techniques. [ABSTRACT FROM PUBLISHER]

In the last decade, data mining techniques have been applied to sensor data in a wide range of application domains, such as healthcare monitoring systems, manufacturing processes, intrusion detection, database management, and others. Many data mining techniques are based on computing the similarity between two sensor data patterns. A variety of representations and similarity measures for multiattribute time series have been proposed in the literature. In this paper, we describe a novel method for computing the similarity of two multiattribute time series based on a temporal version of Smith–Waterman (SW), a well-known bioinformatics algorithm. We then apply our method to sensor data from an eldercare application for early illness detection. Our method mitigates difficulties related to data uncertainty and aggregation that often arise when processing sensor data. The experiments take place at an aging-in-place facility, TigerPlace, located in Columbia, MO, USA. To validate our method, we used data from nonwearable sensor networks placed in TigerPlace apartments, combined with information from an electronic health record. We provide a set of experiments that investigate temporal version of SW properties, together with experiments on TigerPlace datasets. On a pilot sensor dataset from nine residents, with a total of 1902 days and around 2.1 million sensor hits of collected data, we obtained an average abnormal events prediction F-measure of 0.75. [ABSTRACT FROM PUBLISHER]

Self-organization in cells often manifests itself in oscillations and waves. Here, we address deformation waves in protoplasmic droplets of the plasmodial slime mould Physarum polycephalum by modelling and experiments. In particular, we extend a one-dimensional model that considered the cell as a poroelastic medium, where active tension caused mechanochemical waves that were regulated by an inhibitor (Radszuweit et al., 2013). Our extension consists of a simple, qualitative chemical reaction–diffusion model (Brusselator) that describes the regulation of the inhibitor by another biochemical species. The biochemical reaction enhances the formation of mechanochemical waves if the reaction rates and input concentrations are near or inside an oscillatory regime. The period of the waves is found to be controlled by the characteristic oscillation period, whereas their wavelength is set by mechanical parameters. The model also allows for a systematic study of the chemical activity at the onset of mechanochemical waves. We also present examples for pattern formation in protoplasmic droplets of Physarum polycephalum including global oscillations where the central region of the droplets is in antiphase to the boundary zone, as well as travelling and standing wave-like uniaxial patterns. Finally, we apply our model to reproduce these experimental results by identifying the active tension inhibitor with the intracellular calcium concentration in the Physarum droplets and by using parameter values from mechanical experiments, respectively knowledge about the properties of calcium oscillations in Physarum . The simulation results are then found to be in good agreement with the experimental observations. [ABSTRACT FROM AUTHOR]

The grouping problem is critical in collaborative learning (CL) because of the complexity and difficulty in adequate grouping, based on various grouping criteria and numerous learners. Previous studies have paid attention to certain research questions, and the consideration for a number of learner characteristics has arisen. Such a multi-objective grouping problem is with conflicting grouping objectives, involving the benefit objective (e.g. learning achievement) and cost objective (e.g. class rank) which are conflicting in different directions. This study first proposed a novel approach based on the enhancement of a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for facilitating the tradeoff multi-objective grouping optimization, and based on the proposed approach further developed a web-based group support system to help educators for adequate grouping of homogeneous inter-group and heterogeneous intra-group. In addition, the distribution of learners in the class was considered for group formation. Two types of experiments were conducted; one involved a performance analysis against a GA and the Random approach, and the other entailed a study on CL with 90 participants. The experimental results showed the following: 1) The proposed approach is not only more effective than a GA and the Random approach but also more efficient than a GA. 2) As a grouping strategy, the proposed approach can facilitate improved learning performance with statistical significance; in other words, the developed system is able to adequately allocate learners to teams for facilitating CL. [ABSTRACT FROM PUBLISHER]

In this paper, economic- and network-driven distribution generation (DG) placement planning problem is investigated from the local distribution company’s (DISCO’s) viewpoint considering reliability level and power loss of the electrical distribution network. Herein, the feeder’s failure rate is modeled respective to active component of the current flowing through the feeder applying several mathematical functions. In addition, the customers’ load type is modeled and load-model-based power flow is applied instead of conventional power flow. Moreover, in order to achieve realistic results, economic factors such as inflation and interest rates and technical factors including yearly load growth and hourly and daily variations of the load are considered in the planning problem. Furthermore, several analyses are done to calculate amounts of the detriment and error due to unreal modeling of the customers’ load type and the feeder’s failure rate in the DG placement planning problem. It is proved that the unreal modelings can markedly affect the results of the problem and even the optimal locations of the DGs. [ABSTRACT FROM PUBLISHER]

DC microgrid provides a viable and more efficient option to cater for DC loads in the building space and, in particular, data centers. This paper investigates the opportunities associated for utilities to optimize industrial demand response for smart AC and DC microgrid environment thus facilitating distribution utility to reduce peak energy on the existing AC distribution system. A load shifting demand side management (DSM) technique is used to shift AC industrial loads in response to time of day (TOD) tariff. Hence, an attempt has been made to study the impact of DSM strategies with optimal shifting of AC devices in the presence of DC microgrid. Simulations are carried out on a practical distribution system having large industrial loads but it has been assumed that the AC distribution system has DC microgrid with renewables and battery storage systems (BSSs). DSM results for AC distribution grid are compared and analyzed with differing DC microgrid set ups, for example with and without battery storage. It has been observed that the DSM strategy with DC microgrid in the presence of solar renewables and battery storage can substantially reduce average energy cost for demand to response and peak load burden on AC distribution utilities. [ABSTRACT FROM AUTHOR]

Many past researchers have ignored the multi-objective nature of the transit route network design problem (TrNDP), recognizing user or operator cost as their sole objective. The main purpose of this study is to identify the inherent conflict among TrNDP objectives in the design process. The conventional scheme for transit route design is addressed. A route constructive genetic algorithm is proposed to produce a vast pool of candidate routes that reflect the objectives of design, and then, a set covering problem (SCP) is formulated for the selection stage. A heuristic algorithm based on a randomized priority search is implemented for the SCP to produce a set of nondominated solutions that achieve different tradeoffs among the identified objectives. The solution methodology has been tested using Mandl's benchmark network problem. The test results showed that the methodology developed in this research not only outperforms solutions previously identified in the literature in terms of strategic and tactical terms of design, but it is also able to produce Pareto (or near Pareto) optimal solutions. A real-scale network of Rivera was also tested to prove the proposed methodology's reliability for larger-scale transit networks. Although many efficient meta-heuristics have been presented so far for the TrNDP, the presented one may take the lead because it does not require any weight coefficient calibration to address the multi-objective nature of the problem. [ABSTRACT FROM PUBLISHER]

Discretization is one of the most relevant techniques for data preprocessing. The main goal of discretization is to transform numerical attributes into discrete ones to help the experts to understand the data more easily, and it also provides the possibility to use some learning algorithms which require discrete data as input, such as Bayesian or rule learning. We focus our attention on handling multivariate classification problems, where high interactions among multiple attributes exist. In this paper, we propose the use of evolutionary algorithms to select a subset of cut points that defines the best possible discretization scheme of a data set using a wrapper fitness function. We also incorporate a reduction mechanism to successfully manage the multivariate approach on large data sets. Our method has been compared with the best state-of-the-art discretizers on 45 real datasets. The experiments show that our proposed algorithm overcomes the rest of the methods producing competitive discretization schemes in terms of accuracy, for C4.5, Naive Bayes, PART, and PrUning and BuiLding Integrated in Classification classifiers; and obtained far simpler solutions. [ABSTRACT FROM PUBLISHER]

Artificial neural network (ANN) was proposed as an effective method to help multipath ultrasonic flowmeter (UFM) reduce its measurement error when determining the flowrate of complex flow field. However, the effectiveness of the ANN method heavily depends on the network architecture specified by the designer, and also the provided initial weights and layer biases. This hinders the ANN to be widely used in actual UFMs. This paper proposes a genetic algorithm (GA) optimized ANN method (GANN) to be used in UFMs. The GA is utilized to determine ANN architecture based on its efficient parallel advantage in global search to replace traditional trial and error approach or empirical method. In addition, the initial weights and biases are optimized by GA, which is capable to avoid the network getting stuck with local minimum and qualify it for good generalization ability. Tests are implemented on numerical models simulating a six-path UFM installed downstream single or double elbows with complex flow field passing through it. Comparisons between the results of classical Gaussian quadrature, existing ANN, and proposed GANN are given for verification. The flowrate determination error of GANN is only 40% $\sim 62$ % of existing ANN, which is extremely smaller than the Gaussian quadrature method. In addition, the output of GANN is much more stable than existing ANN, as proper initial weights and layer biases will be generated by GA for iteration process even random data are given for training. [ABSTRACT FROM PUBLISHER]

Scaling linkage disequilibrium (LD) based haplotype block recognition to the entire human genome has always been a challenge. The best-known algorithm has quadratic runtime complexity and, even when sophisticated search space pruning is applied, still requires several days of computations. Here, we propose a novel sampling-based algorithm, called S-MIG^++ , where the main idea is to estimate the area that most likely contains all haplotype blocks by sampling a very small number of SNP pairs. A subsequent refinement step computes the exact blocks by considering only the SNP pairs within the estimated area. This approach significantly reduces the number of computed LD statistics, making the recognition of haplotype blocks very fast. We theoretically and empirically prove that the area containing all haplotype blocks can be estimated with a very high degree of certainty. Through experiments on the 243,080 SNPs on chromosome 20 from the 1,000 Genomes Project, we compared our previous algorithm MIG^++ with the new S-MIG^++ and observed a runtime reduction from 2.8 weeks to 34.8 hours. In a parallelized version of the S-MIG ^++ algorithm using 32 parallel processes, the runtime was further reduced to 5.1 hours. [ABSTRACT FROM PUBLISHER]

Silver sulfide (AgS) nanoparticles synthesized using different precursors have been characterized by dynamic light scattering measurements and high-resolution transmission electron microscopy. In addition to AgS nanoparticles, we have detected AgS/Ag heterostructures. Using optical microscopy, we have examined interaction of the nanoparticles with red cells of peripheral blood. The results of the interaction have been shown to depend on the particle size and charge. A red cell solution containing large, negatively charged particles coagulated, whereas small, positively charged AgS nanoparticles were concentrated around red cells. [ABSTRACT FROM AUTHOR]

The uncertain capacitated arc routing problem is a challenging problem in which the demands of tasks, the costs of edges, and the presence of tasks and edges are uncertain. The objective of this problem is to find a robust optimal solution for a finite set of possible scenarios. In this paper, we propose a novel robust optimization approach, called an estimation of distribution algorithm (EDA) with stochastic local search (SLS), to tackle this problem. The proposed method integrates an EDA with a novel two phase SLS procedure to minimize the maximal total cost over a set of different scenarios. The SLS procedure avoids excessive fitness evaluations of unpromising moves in local search. Our experimental results on two sets of benchmark problems (a total of 55 problem instances) showed that the proposed approach outperformed existing state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, a novel self-learning gene expression programming (GEP) methodology named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. In contrast to the existing GEP variants, the proposed SL-GEP features a novel chromosome representation in which each chromosome is embedded with subfunctions that can be deployed to construct the final solution. As part of the chromosome, the subfunctions are self-learned or self-evolved by the proposed algorithm during the evolutionary search. By encompassing subfunctions or any partial solution as input arguments of another subfunction, the proposed SL-GEP facilitates the formation of sophisticated, higher-order, and constructive subfunctions that improve the accuracy and efficiency of the search. Further, a novel search mechanism based on differential evolution is proposed for the evolution of chromosomes in the SL-GEP. The proposed SL-GEP is simple, generic and has much fewer control parameters than the traditional GEP variants. The proposed SL-GEP is validated on 15 symbolic regression problems and six even-parity problems. Experimental results show that the proposed SL-GEP offers enhanced performances over several state-of-the-art algorithms in terms of accuracy and search efficiency. [ABSTRACT FROM PUBLISHER]

Fuzzy cognitive maps (FCMs) are cognition fuzzy influence graphs, which are based on fuzzy logic and neural networks. Many automated learning algorithms have been proposed to reconstruct FCMs from data, but most learned maps using such methods are much denser than those constructed by human experts. To this end, we first model the FCM learning problem as a multiobjective optimization problem, and then propose a multiobjective evolutionary algorithm, labeled as MOEA-FCM, to learn FCM models. MOEA-FCM is able to learn FCMs with varying densities at the same time from input historical data, which can provide candidate solutions with different properties for decision makers. In the experiments, the performance of MOEA-FCM is validated on both synthetic and real data with varying sizes and densities. The experimental results demonstrate the efficiency of MOEA-FCM and show that MOEA-FCM can not only reconstruct FCMs with high accuracy without expert knowledge, but also create a diverse Pareto optimal front which consists of FCMs with varying densities. The significance of this study is that decision makers can choose different FCM models provided by MOEA-FCM based on their practical requirements. [ABSTRACT FROM PUBLISHER]

A hybrid framework composed of two stages for gene selection and classification of DNA microarray data is proposed. At the first stage, five traditional statistical methods are combined for preliminary gene selection (Multiple Fusion Filter). Then, different relevant gene subsets are selected by using an embedded Genetic Algorithm (GA), Tabu Search (TS), and Support Vector Machine (SVM). A gene subset, consisting of the most relevant genes, is obtained from this process, by analyzing the frequency of each gene in the different gene subsets. Finally, the most frequent genes are evaluated by the embedded approach to obtain a final relevant small gene subset with high performance. The proposed method is tested in four DNA microarray datasets. From simulation study, it is observed that the proposed approach works better than other methods reported in the literature. [ABSTRACT FROM PUBLISHER]

Due to the demands of the fast identifying method of the typical blood cells in scientific research and clinical examination, a new quick sub-structure imaging approach is presented. In this method, two orthogonal phase images of a cell's model are obtained by simulation experiment at first. According to the characteristics of the phase mutation which occurs on the boundary between nucleus and cytoplasm, the outlines of the model, including the ones of the sub-structure, could be extracted by edge detection. In view of the symmetry of the outlines, the geometrical model of the sample may be approximately segmented into several parts of revolving bodies. Furthermore, the revolving body could be built by rotating an outline along the perpendicular one. And then, the rest revolving bodies could be reconstructed by the same way. After all, the 3D sub-structure image is built based on their space structure characteristic. The feasibility of this method is demonstrated by a simulated monocyte, and the result agrees well with the real one. It shows that the method is very efficient and the characteristic of typical blood cells can be described. [ABSTRACT FROM AUTHOR]

In this paper, three metaheuristic techniques have been developed to propose a safe and economic grounding system for the future power plant of Labreg situated in Khenchela City (400 km east of Algiers). The corresponding algorithms have been elaborated using particle swarm optimization (PSO), genetic algorithm optimization (GAO) and hybrid particle swarm genetic algorithm optimization (HPSGAO). The aim is to minimize the cost of the considered grounding system basing on the optimal decision of its construction and geometrical parameters in accordance with the security restrictions required by the ANSI/IEEE Standard 80-2000. A new mathematical model has been proposed for the cost function. This later includes the number of conductors, conductor dimension, grid depth, number of rods, length of rods, total area of excavation, and revetment. The results show that the HPSGAO technique presents lower values of the cost than those obtained using GAO and PSO methods. The good accordance between HPSGAO technique safety parameters and those of the CYMGrd code confirms the efficiency of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

This paper studies a lane reservation problem for large sport events in big cities. Such events require organizers to deliver certain people and materials from athlete villages to geographically dispersed venues within a given travel duration. A lane reservation strategy is usually adopted in this circumstance to ensure that time-critical transportation tasks can be completed despite heavy urban traffic congestion. However, it causes negative impact on normal traffic. The problem aims to optimally select and reserve some lanes in a transportation network for the exclusive use of the tasks such that the total traffic impact is minimized. To solve the problem, we first develop an improved integer linear program. Then, its properties are analyzed and used to reduce the search space for its optimal solutions. Finally, we develop a fast and effective quantum-inspired evolutionary algorithm for large-size problems. Computational results on instances with up to 500 nodes in the network and 50 tasks show that the proposed algorithm is efficient in yielding high-quality solutions within a relatively short time. [ABSTRACT FROM PUBLISHER]

In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is proposed to solve the unit commitment (UC) problem as a multiobjective optimization problem (MOP) considering minimizing cost and emission as the multiple objectives. Since UC problem is a mixed-integer optimization problem, a hybrid strategy is integrated within the framework of MOEA/D such that genetic algorithm (GA) evolves the binary variables, while differential evolution (DE) evolves the continuous variables. Further, a novel nonuniform weight-vector distribution (NUWD) strategy is proposed and an ensemble algorithm based on combination of MOEA/D with uniform weight-vector distribution (UWD) and NUWD strategy is implemented to enhance the performance of the presented algorithm. Extensive case studies are presented on different test systems and the effectiveness of the hybrid strategy, the NUWD strategy, and the ensemble algorithm is verified through stringent simulated results. Further, exhaustive benchmarking against the algorithm proposed in the literature is presented to demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

One of the new challenges of biometric systems based on face analysis is kinship verification. Little efforts have been done in spite of the importance and functionality of this subject. Most of existing methods have been trying to exploit and represent techniques based on metric learning to increase verification rate, paying no attention to the effect of the features extracted from the faces. Despite the previous methods exploiting simple local features, we have focused on the combination and selection of effective features in this paper. To this end, local and global features were combined to describe the face images in a better way. The effective and discriminative features were selected using the kinship genetic algorithm and then fulfilled kinship verification. The proposed method is tested and analysed on the standard and big datasets KinFaceW-I and KinFaceW-II, and verification rates of 81.3% and 86.15% were obtained respectively. [ABSTRACT FROM PUBLISHER]

In a heterogeneous Wireless Sensor Network (WSN), factors such as initial energy, data processing capability, etc. greatly influence the network lifespan. Despite the success of various clustering strategies of WSN, the numerous possible sensor clusters make searching for an optimal network structure an open challenge. In this paper, we propose a Genetic Algorithm based method that optimizes heterogeneous sensor node clustering. Compared with five state-of-the-art methods, our proposed method greatly extends the network life, and the average improvement with respect to the second best performance based on the first-node-die and the last-node-die is 33.8% and 13%, respectively. The balanced energy consumption greatly improves the network life and allows the sensor energy to deplete evenly. The computational efficiency of our method is comparable to the others and the overall average time across all experiments is 0.6 seconds with a standard deviation of 0.06. [ABSTRACT FROM PUBLISHER]

Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models. [ABSTRACT FROM AUTHOR]

Different types of distributed generation (DG) are broadly used and optimally placed in a distribution system to improve its performance. Since the network configuration affects the system operational conditions, the network reconfiguration and DG placement should be manipulated simultaneously. Nevertheless, the complexity of the problem may prevent from achieving the optimal solution. This paper presents a novel hybrid method of metaheuristic and heuristic algorithms, in order to boost robustness and shorten the computational runtime to achieve network minimum loss configuration in the presence of DGs. The developed backward/forward power flow is adopted to consider the PV(Q) model of DG. Moreover, different patterns of load types are taken into consideration to perform a practical study. To assess the capabilities of the proposed method, simulations are carried out on IEEE 33-bus and 83-bus practical distribution network of Taiwan Power Company. Furthermore, the proposed method is applied to a 33-bus unbalanced distribution network to verify its applicability in unbalanced distribution systems. The obtained results demonstrate the effectiveness of the proposed method to find optimal status of switches, as well as locations and sizes of DG units, in a rather shorter time than other approaches in the literature. [ABSTRACT FROM PUBLISHER]

Blockmodel problem (BMP) deals with identifying structural similarities or equivalences among entities which, in turn, provide many insights into the structures or patterns of complex networks such as social networks. The objective of this problem is to find a small number of large blocks containing structural similarities or equivalences among entities in a given graph representing a complex network. In this paper, we present an evolutionary approach and a swarm intelligence approach for the BMP. The evolutionary approach consists of a steady-state grouping genetic algorithm (GA), whereas the swarm intelligence approach is based on the artificial bee colony (ABC) algorithm. The BMP is a grouping problem, i.e., a problem whose objective is to find an optimal assignment of entities according to a given fitness function into different groups subject to some constraints. Grouping GAs are especially designed to handle grouping problems as the traditional GA suffers from the problem of redundancy, context insensitivity, and schema disruption while handling grouping problems. Our ABC algorithm is also designed in such a manner that it tries to preserve grouping information as far as possible in order to find high-quality solutions. To our knowledge, this is the first application of the ABC algorithm for a problem with a variable number of groups. Computational results show the effectiveness of our approaches. [ABSTRACT FROM PUBLISHER]

In this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets. [ABSTRACT FROM PUBLISHER]

Shape representation is an extremely important and longstanding problem in the field of pattern recognition. Closed contour, which refers to shape contour, plays a crucial role in the comparison of shapes. Because shape contour is the most stable, distinguishable, and invariable feature of an object, it is useful to incorporate it into the recognition process. This paper proposes a method based on genetic algorithms. The proposed method can be used to identify the most common contour fragments, which can be used to represent the contours of a shape category. The common fragments clarify the particular logics included in the contours. This paper shows that the explicit representation of the shape contour contributes significantly to shape representation and object recognition. [ABSTRACT FROM PUBLISHER]

The multiple traveling salesman problem (MTSP) is an important combinatorial optimization problem. It has been widely and successfully applied to the practical cases in which multiple traveling individuals (salesmen) share the common workspace (city set). However, it cannot represent some application problems where multiple traveling individuals not only have their own exclusive tasks but also share a group of tasks with each other. This work proposes a new MTSP called colored traveling salesman problem (CTSP) for handling such cases. Two types of city groups are defined, i.e., each group of exclusive cities of a single color for a salesman to visit and a group of shared cities of multiple colors allowing all salesmen to visit. Evidences show that CTSP is NP-hard and a multidepot MTSP and multiple single traveling salesman problems are its special cases. We present a genetic algorithm (GA) with dual-chromosome coding for CTSP and analyze the corresponding solution space. Then, GA is improved by incorporating greedy, hill-climbing (HC), and simulated annealing (SA) operations to achieve better performance. By experiments, the limitation of the exact solution method is revealed and the performance of the presented GAs is compared. The results suggest that SAGA can achieve the best quality of solutions and HCGA should be the choice making good tradeoff between the solution quality and computing time. [ABSTRACT FROM PUBLISHER]

Editor's notes: [ABSTRACT FROM PUBLISHER]

Nucleosomes and the free linker DNA between them assemble the chromatin. Nucleosome positioning plays an important role in gene transcription regulation, DNA replication and repair, alternative splicing, and so on. With the rapid development of ChIP-seq, it is possible to computationally detect the positions of nucleosomes on chromosomes. However, existing methods cannot provide accurate and detailed information about the detected nucleosomes, especially for the nucleosomes with complex configurations where overlaps and noise exist. Meanwhile, they usually require some prior knowledge of nucleosomes as input, such as the size or the number of the unknown nucleosomes, which may significantly influence the detection results. In this paper, we propose a novel approach DPNuc for identifying nucleosome positions based on the Dirichlet process mixture model. In our method, Markov chain Monte Carlo (MCMC) simulations are employed to determine the mixture model with no need of prior knowledge about nucleosomes. Compared with three existing methods, our approach can provide more detailed information of the detected nucleosomes and can more reasonably reveal the real configurations of the chromosomes; especially, our approach performs better in the complex overlapping situations. By mapping the detected nucleosomes to a synthetic benchmark nucleosome map and two existing benchmark nucleosome maps, it is shown that our approach achieves a better performance in identifying nucleosome positions and gets a higher $F$ -score. Finally, we show that our approach can more reliably detect the size distribution of nucleosomes. [ABSTRACT FROM PUBLISHER]

Phased haplotype information is crucial in our complete understanding of differences between individuals at the genetic level. Given a collection of DNA fragments sequenced from a homologous pair of chromosomes, the problem of single individual haplotyping (SIH) aims to reconstruct a pair of haplotypes using a computer algorithm. In this paper, we encode the information of aligned DNA fragments into a two-locus linkage graph and approach the SIH problem by vertex labeling of the graph. In order to find a vertex labeling with the minimum sum of weights of incompatible edges, we develop a fast and accurate heuristic algorithm. It starts with detecting error-tolerant components by an adapted breadth-first search. A proper labeling of vertices is then identified for each component, with which sequencing errors are further corrected and edge weights are adjusted accordingly. After contracting each error-tolerant component into a single vertex, the above procedure is iterated on the resulting condensed linkage graph until error-tolerant components are no longer detected. The algorithm finally outputs a haplotype pair based on the vertex labeling. Extensive experiments on simulated and real data show that our algorithm is more accurate and faster than five existing algorithms for single individual haplotyping. [ABSTRACT FROM PUBLISHER]

The nondestructive characterization of biological liquids and cell suspensions using electromagnetic waves in the microwave frequency range calls for accurate and sensitive measurement devices. Especially when reducing the sample volume, the sensitivity becomes a critical design parameter for broadband sensors. In this paper, a miniaturized transmission-line sensor based on a coplanar waveguide is used to characterize the permittivity of nanoliter volumes of biologically relevant liquids and cell suspensions. The sensor’s sensitivity is increased by means of electrically small discontinuities within the sensing section. The biological samples are guided across the sensor in a microfluidic channel, which is fabricated using microsystems technology. The sensor is used between 850 MHz and 40 GHz to detect the broadband permittivity of liquid samples such as aqueous salt and protein solutions. The experimentally detected contrast between living and dead Chinese hamster ovary cells in suspension is significant despite the small sample volume. [ABSTRACT FROM PUBLISHER]

The physical properties of cement are strongly influenced by the development of microstructure and cement hydration. Therefore, the investigation of microstructure for cement paste enables us to understand the hydration process and to predict the physical properties. However, the unreliability of phase classification and segmentation in an image affect the description of microstructure, as well as the prediction of properties and the simulation of hydration. This paper studies the dynamic relationship between microstructure and physical properties from the image itself. The relationship between compressive strength and microstructure image features is built as the form of image feature kinetics using gene expression programming from observed microtomography images. A similarity weight tournament selection is also proposed to increase the diversity of population and improve the performance. Experimental results manifest that the evolved image feature kinetics not only perform well in fitting training data but also exhibit superior generalization ability. [ABSTRACT FROM PUBLISHER]

Carpooling is a means of vehicle sharing by which drivers share their cars with one or more riders whose travel itineraries are similar to their own. As such, carpooling can be an effective way to ease traffic congestion. In this paper, we first present an intelligent carpool system based on the service-oriented architecture. Second, we propose a fuzzy-controlled genetic-based carpool algorithm by using the combined approach of the genetic algorithm and the fuzzy control system, with which to optimize the route and match assignments of the providers and the requesters in the intelligent carpool system. In regard to the quality of the match solutions and processing time, the exhaustive algorithm, the random matching algorithm, and the standard genetic algorithm are applied and their results compared with those produced by our proposed algorithm. Our experimental results proved that the proposed fuzzy-controlled genetic-based carpool algorithm is capable of consistently finding carpool route and matching results that are among the most optimal solutions that can be obtained via the exhaustive algorithm and, thus, outperforming all other compared methods in regard to match quality. In addition, the proposed algorithm is also able to operate with significantly less computational time than does the exhaustive algorithm and random matching algorithm. [ABSTRACT FROM PUBLISHER]

Network real-time kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real-time, and it is enabled by a network of continuously operating reference stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a memetic algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper, we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA. [ABSTRACT FROM PUBLISHER]

In this paper, we present novel experimental data of the magnetic permeabilization of the mammalian cells in high pulsed magnetic fields up to 16.4 T. Two designs of the multilayer solenoid-type pulsed inductors, which were adapted for the biological experiments, are presented. The induced electric field is analyzed using the finite-element method analysis. The experimental methodology and the pulsed magnetic field setups are overviewed. The experimental results with mouse myeloma cell line Sp2/0 in pulsed magnetic field after pulse bursts and single-pulse treatment are presented. The SYTOX Green dye was used for the estimation of the permeabilization process efficacy. It has been shown that the 16.4 T submillisecond low ( < 1.1 \,\times \, 10^\mathrm \mathbf 5 T/s) dB/dt magnetic field pulses do not cause any observable dye uptake changes using the proposed methodology. The short repetitive microsecond range pulses ( dB/dt>0.8 \,\times \, 10^6 T/s) result in an average up to 2.6% ± 0.5% increase of the dye uptake. [ABSTRACT FROM AUTHOR]

Scheduling for wafer fabrication of advanced technology nodes entails complicated constraints such as limited waiting times. Focusing on real settings, this paper aims to develop a novel genetic algorithm of multi-subpopulation parameters with hybrid estimation of distribution (MSPHEDA) to solve the present problem effectively and efficiently. To estimate the validity of this approach, ten scenarios were simulated on the basis of empirical data as the basis to compare the performance of MSPHEDA and other heuristic methods for minimizing makespan and reducing the total exceeded limited waiting time. The results have shown practical viability of the proposed approach. [ABSTRACT FROM AUTHOR]

A design of experiment (DOE) strategy applied to multi‐objective optimisation is proposed in order to evaluate the influence of design variables variations to optimised quantities. A secondary objective function is the sensitivity of a primary objective function to design variable variations evaluated by means of DOE strategy. The optimisation problem includes also a third objective function that considers device constraint because of technological limitations on power generator. The proposed case study deals with the design of an electromagnetic device that will be used to carry out laboratory experiments on magneto‐fluid hyperthermia, that is, a clinic treatment for cancer cure. The induction system is designed to apply a controlled time‐varying magnetic field to biological cells, cultured in Petri dish and mixed with magnetic nanoparticles. This study presents an original cost‐effective method of multi‐objective design optimisation taking into account design uncertainties. [ABSTRACT FROM AUTHOR]

The encoding or representation scheme in evolutionary algorithms is very important because it can greatly affect their performance. Most previous evolutionary algorithms for solving graph problems have traditionally used a vertex-based encoding in which each gene corresponds to a vertex. In this paper, addressing the well-known maximum cut problem, we introduce an edge-set encoding based on the spanning tree—a kind of edge-based encoding. In our encoding scheme, each gene corresponds to an edge subset derived from a spanning tree. In contrast to a traditional edge-based encoding in which each gene corresponds to only one edge, our encoding scheme has the advantage of representing only feasible solutions, so there is no need to apply a repair step. We present a genetic algorithm based on this new encoding. We have conducted various experiments on a large set of test graphs including commonly used benchmark graphs and have obtained performance improvement on sparse graphs, which frequently appear in real-world applications such as social networks and systems biology, in comparison with a scheme using a vertex-based encoding. [ABSTRACT FROM PUBLISHER]

We utilize a for-profit aggregator-based residential demand response (DR) approach to the smart grid resource allocation problem. The aggregator entity, using a given set of schedulable residential customer assets (e.g., smart appliances), must set a schedule to optimize for a given objective. Here, we consider optimizing for the profit of the aggregator. To encourage customer participation in the residential DR program, a new pricing structure named customer incentive pricing (CIP) is proposed. The aggregator profit is optimized using a proposed heuristic framework, implemented in the form of a genetic algorithm, that must determine a schedule of customer assets and the CIP. To validate our heuristic framework, we simulate the optimization of a large-scale system consisting of 5555 residential customer households and 56 642 schedulable assets using real-pricing data over a period of 24-h. We show that by optimizing purely for economic reasons, the aggregator can enact a beneficial change on the load profile of the overall power system. [ABSTRACT FROM PUBLISHER]

Lab-on-a-chip micro-devices utilizing electric fieldmediated particle movement provide advantages over current cell rotation techniques due to the flexibility in configuring micro-electrodes. Recent technological advances in micro-milling, three-dimensional (3D) printing and photolithography have facilitated fabrication of complex micro-electrode shapes. Using the finite-element method to simulate and optimize electric field induced particle movement systems can save time and cost by simplifying the analysis of electric fields within complex 3D structures. Here we investigated different 3D electrode structures to obtain and analyse rotational electric field vectors. Finiteelement analysis was conducted by an electric current stationary solver based on charge relaxation theory. Highresolution data were obtained for three-, four-, six- and eight-cylindrical electrode arrangements to characterize the rotational fields. The results show that increasing the number of electrodes within a fixed circular boundary provides larger regions of constant amplitude rotational electric field. This is a very important finding in practice, as larger rotational regions with constant electric field amplitude make placement of cells into these regions, where cell rotation occurs, a simple task - enhancing flexibility in cell manipulation. Rotation of biological particles over the extended region would be useful for biotechnology applications which require guiding cells to a desired location, such as automation of nuclear transfer cloning. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a new method for the better assessment of cytoplasm conductivity, which is critical to the development of electroporation protocols as well as insight into fundamental mechanisms underlying electroporation. For this goal, we propose to use nanosecond electrical pulses to bypass the complication of membrane polarization and a single cell to avoid the complication of the application of the “mixing formulas.” Further, by suspending the cell in a low-conductivity medium, it is possible to force most of the sensing current through the cytoplasm for a more direct assessment of its conductivity. For proof of principle, the proposed technique was successfully demonstrated on a Jurkat cell by comparing the measured and modeled currents. The cytoplasm conductivity was best assessed at 0.32 S/m and it is in line with the literature. The cytoplasm conductivity plays a key role in the understanding of the basis mechanism of the electroporation phenomenon, and in particular, a large error in the cytoplasm conductivity determination could result in a correspondingly large error in predicting electroporation. Methods for a good estimation of such parameter become fundamental. [ABSTRACT FROM AUTHOR]

In this paper, we report a disposable microfluidic impedance cytometer attached on a reusable printed circuit board (PCB) with dielectric layer for single cell detection. The cover slide acts as a dielectric layer which allows the AC signal to sense the impedance change cross the microfluidic channel. The microfluidic chip can be disposed; while the bottom PCB with pre-deposited electrodes is reusable after each sample test. We experimentally demonstrate that the developed device is able to selectively detect HeLa cells, a type of umor cells, and ignore Red Blood Cells (RBCs). Integration of low cost and disposable microfluidic chip with reusable PCBs widely available in the industry is of great potential in point of care diagnosis of cancer, especially in the developing countries. [ABSTRACT FROM AUTHOR]