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Rearrangements are mutations that can change the organization of a genome, but not its content. Examples are inversions of DNA segments, translocations of chromosome ends, fusions and fissions of chromosomes. All mentioned rearrangements can be represented by the generic Double Cut and Join (DCJ) operation. However, the DCJ operation also allows circular chromosomes to be created at intermediate steps, even if the compared genomes are linear. In this case it is more plausible to consider a restriction in which the reincorporation of a circular chromosome has to be done immediately after its creation. We call these two consecutive operations an $ER$ composition. It has been shown that an $ER$ composition mimics either an internal block interchange (when two segments in the same chromosome exchange their positions), or an internal transposition (the special case of a block interchange when the two segments are adjacent). The DCJ distance of two genomes is the same, regardless of this restriction, and can be computed in linear time. For comparing two genomes with unequal contents, in addition to rearrangements we have to allow insertions and deletions of DNA segments—named indels. It is already known that the distance in the model combining DCJ and indel operations can be exactly computed. Again, for linear genomes it would be more plausible to adopt a restricted version with $ER$ compositions. This model was studied recently by da Silva et al. (BMC Bioinformatics 13, Suppl. 19, S14, 2012), but only an upper bound for the restricted DCJ-indel distance was provided. Here we first solve an open problem posed in that paper and present a very simple proof showing that the distance, which can be computed in linear time, is the same for both the unrestricted and the restricted DCJ-indel models. We then give a simpler algorithm for computing an optimal restricted DCJ-indel sorting scenario in $O(n \;\log\; n)$ time. We also relate the DCJ-indel distance to the restricted DCJ-substitution distance, which instead of indels considers a more powerful operation that allows the substitution of a DNA segment by another DNA segment. We show that the DCJ-indel distance is a 2-approximation for the restricted DCJ-substitution distance. [ABSTRACT FROM PUBLISHER]

Facility location allocation (FLA) is considered as the problem of finding optimally a facility's location with the maximum customer satisfaction, the maximum profit of investors of the facility, and the minimum transportation cost of its oriented-customers. In practice, some factors of the FLA problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus the problem features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic profit and cost issues of FLA. However, a decision-maker hopes to obtain the specific profit of investors of building facility and meanwhile to minimize the cost of target customers. To handle this issue via a more practical manner, it is essential to address the cost-profit tradeoff issue of FLA. Moreover, some region constraints can greatly influence FLA. By taking the vehicle inspection station as a typical automotive service enterprise example, this work presents new stochastic cost-profit tradeoff FLA models with region constraints. A hybrid algorithm integrating stochastic simulation and Genetic Algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

We develop an evolutionary fuzzy proportional-integral-derivative (PID) controller for a permanent magnet synchronous motor (PMSM). We first consider a fuzzy PID control design problem based on the common control engineering knowledge that good transient performances can be obtained by increasing the P and I gains and decreasing the D gain when the transient error is big. Then we give an evolutionary algorithm (EA) to autotune the control parameters of the fuzzy PID controller. We implement the proposed EA-based fuzzy PID control controller in real time on a Texas Instruments TMS320F28335 floating-point DSP. We also give simulation and experimental results to show the effectiveness of the proposed intelligent digital control system under abrupt load torque variation using a prototype PMSM. [ABSTRACT FROM PUBLISHER]

The spiral-shaped microchannel geometry of a microfluidic system was investigated for a separation process of suspension-adapted Chinese hamster ovary cells. The fluid phase of the transported cell suspension thereby consists of the culture medium TC42, with added glutamine, and saltwater. Paths of fictive cells reproduced as massless and dimensionless particles, i.e., certain flow lines of the fluid phase, have been analyzed according to their dynamic motion behavior and exposure to shear stresses with single-phase, steady-state computational fluid dynamic simulation results. A parameter study has been made by varying the volume flow. The vertical motion component of the paths inside the inlet and the spiral geometry are also in main focus contrary to many publications, in which the lateral component plays the major role. Two periods with a local path accumulation have been identified. The shear stresses in terms of the energy dissipation rate were calculated along the paths to estimate possible cell loading. At the highest modeled volume flow, a cell lysis probability was estimated to be likely for a small cell fraction. [ABSTRACT FROM AUTHOR]

The next-generation wireless communication networks are envisioned to offer many multimedia services such as audio/video clips, mobile TV, web browsing, video conference, etc., with diverse quality of service (QoS) requirements. Multicasting/broadcasting has been recognized as an emerging and enabling technique for such multimedia transmissions over wireless networks. By employing multicast transmission, a base station can transmit the same data content to several groups of users requiring flexible QoS. In this paper, we investigate subchannel and power allocation problems in an OFDM-based wireless multicast system. With the goal of maximizing the total system capacity, a low complexity, novel genetic algorithm aided efficient subchannel allocation scheme taking into account the constraints of total available bandwidth, proportional data rate fairness and total transmit power budget at the base station is proposed. The subchannel allocation is then followed by optimal power allocation. Unlike previous work where either no fairness or fairness based on number of subchannels allocated to the different groups was considered, we incorporate a set of system parameters in the problem formulation such that the ratio of the group data rates strictly follow the set of system parameters after resource allocation. Simulation results show that the proposed method obtains higher sum capacities while maintaining proportional data rate fairness among different multicast groups, without being awfully complex. [ABSTRACT FROM PUBLISHER]

Distribution system reconfiguration (DSR) is a multi-objective, nonlinear problem. This paper introduces a new, fast, nondominated sorting genetic algorithm (FNSGA) for the purpose of solving the DSR problem in normal operation by satisfying all objectives simultaneously with a relatively small number of generations and relatively short computation time. The objectives of the problem are to minimize real power losses and improve the voltage profile and load balancing index with minimum switching operations. Instead of generating several ranks from the nondominated set of solutions, this algorithm deals with only one rank; then the most suitable solution is chosen according to the operator's wishes. If there is no preference and all objectives have the same degree of importance, the best solution is determined by simply considering the sum of the normalized objective values. Also, a guided mutation operation is applied instead of a random one to speed up convergence. Radial system topology is satisfied using graph theory by formulating the branch-bus incidence matrix (BBIM) and checking the rank of each topology. To test the algorithm, it was applied to two widely studied test systems and a real one. The results show the efficiency of this algorithm as compared to other methods in terms of achieving all the goals simultaneously with reasonable population and generation sizes and without using a mutation rate, which is usually problem-dependent. [ABSTRACT FROM PUBLISHER]

The electrical properties of biological cells, tissues, and organs studied in electrophysiology not only provide information about the normal/abnormal activities of human bodies but also provide a means to directly restore, repair, replace, or recreate physiological functions in humans to improve quality of life. For example, cardiac pacemakers, implanted in millions of patients, deliver electrical pulses into the right atrium and/or the right/both ventricles to control abnormal heart rhythms. Electrical activities of neurons such as action potentials (specific waveforms of voltage fired by neurons) have been studied for neurodisorders. Recordings of electric signals spatially and temporally around the brain and central nervous system, such as electroencephalography [(EEG) electrical activities along the scalp from the brain] and evoked potential responses (measurement of the time and waveform changes for nerves in other parts of the body in response to electrical stimulation of the brain), help to understand brain functions and diagnose diseases such as multiple sclerosis (nerve cells in the brain and spinal cord gradually degrade causing problems in muscle control and cognition). As activation to targeted tissues, neurostimulation triggers neurons with electrical voltages or currents to excite signal propagation in the nervous system to restore functions. Sensations such as hearing and vision can be restored by cochlear or retinal implants. Tremors can be controlled with deep brain stimulation in the subthalamic nucleus and the globus pallidus interna areas to manage Parkinson?s Disease and dystonia (both with symptoms of involuntary muscle contractions and tremors). Chronic pain can be inhibited by electrically stimulating the periaqueductal gray and periventricular gray areas in the brain for nociceptive pain, and the ventral posterolateral/posteromedial nucleus for neuropathic pain. Brain?computer interfaces have been developed for motor neuroprosthetics, such as robotic hands, arms, or legs that can be controlled by thoughts with decoded action potentials or EEG signals from the brain for patients with paralysis. Recently, experiments on electrical stimulation in the uperolateral branch of the medial forebrain bundle have found a significant antidepressant effect for major depression, especially treatment-resistant depression. [ABSTRACT FROM PUBLISHER]

In the context of energy saving and carbon emission reduction, the electric vehicle (EV) has been identified as a promising alternative to traditional fossil fuel-driven vehicles. Due to a different refueling manner and driving characteristic, the introduction of EVs to the current logistics system can make a significant impact on the vehicle routing and the associated operation costs. Based on the traveling salesman problem, this paper proposes a new optimal EV route model considering the fast-charging and regular-charging under the time-of-use price in the electricity market. The proposed model aims to minimize the total distribution costs of the EV route while satisfying the constraints of battery capacity, charging time and delivery/pickup demands, and the impact of vehicle loading on the unit electricity consumption per mile. To solve the proposed model, this paper then develops a learnable partheno-genetic algorithm with integration of expert knowledge about EV charging station and customer selection. A comprehensive numerical test is conducted on the 36-node and 112-node systems, and the results verify the feasibility and effectiveness of the proposed model and solution algorithm. [ABSTRACT FROM PUBLISHER]

The genomes of different human beings are similar. There are only a relatively small number of genetic differences between people. The genetic differences between people are very worthy of study. Researchers have proposed the fixation index FST measurement to find the single nucleotide polymorphisms (SNPs) which can reflect human population differences. However, most SNPs have interactions and they work together, which leads to the differences among human populations. The number of all possible m-locus combinations chosen from n SNPs grows exponentially. Most methods concern on 2-locus interactions. In this paper, we propose a novel method to find a new coordinate system under which the energy distributions of different populations are quite different. We select out candidate SNPs from n SNPs by using the information of the axes in the coordinate system. The number of candidate SNPs is small, thus SNP-SNP interactions can be searched efficiently. The method can also find interactions of more than two loci. These interactions should be able to reflect the evolution of human populations from another way. The numbers of SNP-SNP interactions are regarded as the differences between pairwise populations and a hierarchical clustering algorithm is used to construct the evolutionary tree. In the experiments, we apply the method to SNP data of four chromosomes separately and the trees constructed on these four chromosomes are highly consistent. Furthermore, the trees are also consistent with previous studies, which indicates that evolutionary information is well mined. The method provides a new insight to analyze the human population differences. [ABSTRACT FROM PUBLISHER]

In this paper, a power/ground (P/G) pin optimization method using genetic algorithm (GA) is proposed for large-scale high-pin-count ball grid array (BGA) packages. Two objective functions are derived for signal integrity and power integrity, respectively. A general optimization flow is presented, where the basic concepts of GA optimization are introduced and some important considerations for package design are demonstrated. A customized GA flow and two accelerating strategies are developed to improve the efficiency of the optimization procedure. Using GA optimization, the P/G pin assignment of a $40\times 40$ BGA package with blocks of core, inputs/outputs and differential pairs can be generated in a few tens of minutes automatically. [ABSTRACT FROM PUBLISHER]

Grade, due date, priority, and demand are attributes of magnetic material products. Planners are required to seek the optimal combination of production work orders to minimize cost and improve efficiency based on these attributes. The magnetic material group furnace optimization problem is a generalization of the 1-D bin-packing problem wherein bins of varying sizes are used. Bin sizes are determined by the grade and demand of the grouped work orders. A mathematical model is established to solve the magnetic material group furnace optimization problem by using a specialized genetic algorithm (SGA). In SGA, an initial population generation method is designed by following the sort criteria of the earliest completion date. The furnace charging weight is set according to several rules derived from work order attributes. An elite strategy and an improved greedy three-crossover operator are introduced to enhance convergence speed and precision. In addition, a reverse operator is applied to exploit the proposed algorithm. Simulation results based on practical production data show that the established model is suitable and that the presented algorithm is effective. [ABSTRACT FROM PUBLISHER]

Traffic congestion has been a serious problem in many urban areas around the world. Carpooling is one of the most effective solutions to traffic congestion. It consists of increasing the occupancy rate of cars by reducing the empty seats in these vehicles effectively. In this paper, an advanced carpool system is described in detail and called the intelligent carpool system (ICS), which provides carpoolers the use of the carpool services via a smart handheld device anywhere and at any time. The carpool service agency in the ICS is integrated with the abundant geographical, traffic, and societal information and used to manage requests. For help in coordinating the ride matches via the carpool service agency, we apply the genetic algorithm to propose the genetic-based carpool route and matching algorithm (GCRMA) for this multiobjective optimization problem called the carpool service problem (CSP). The experimental section shows that the proposed GCRMA is compared with two single-point methods: the random-assignment hill climbing algorithm and the greedy-assignment hill climbing algorithm on real-world scenarios. Use of the GCRMA was proved to result in superior results involving the optimization objectives of CSP than other algorithms. Furthermore, our GCRMA operates with significantly a small amount of computational complexity to response the match results in the reasonable time, and the processing time is further reduced by the termination criteria of early stop. [ABSTRACT FROM PUBLISHER]

Reliable knowledge of in vivo joint kinematics is fundamental in clinical medicine. Fluoroscopic motion tracking theoretically permits a millimeter/degree level of accuracy in 3-D joint motion analysis, but the reliability of the local optimization algorithm [Levenberg–Marquardt (LMA)], typically used for the pose estimation, is highly operator dependent. A new memetic algorithm (MA), hybridizing global evolution and a local search metaphor for learning, is proposed to automate the analysis and improve its reliability and robustness. The performance of MA was assessed for in silico and in vivo elbow kinematics, with and without user supervision. The best learning strategy between Lamarckian and Baldwinian evolution was identified. MA's accuracy and repeatability was quantified and compared with LMA's. The algorithm performed best using a partial Lamarckian learning strategy. The geometric symmetry of analyzed bony segments influenced the accuracy, whereas the absolute bone pose with respect to the projection geometry affected the repeatability. In contrast to LMA, MA provided robust, repeatable, and operator independent pose estimations, even for in vivo analyses. The pose can be automatically estimated with errors lower than 1 mm and 1 ^\circ for all the pose parameters except the depth position, if the investigated motion task avoids symmetric bony projection silhouettes. [ABSTRACT FROM AUTHOR]

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite. [ABSTRACT FROM PUBLISHER]

We propose a system to transform any temporal image sequence into a comics-based presentation, as an effective and interesting storytelling manner. Three main components, including page allocation, layout selection, and speech balloon placement, are respectively formulated as optimization problems, and systematic approaches are proposed to find solutions. Page allocation is viewed as a labeling problem, and the best solution is determined by the genetic algorithm. Importance values of images and predefined layouts are both represented in vector forms, and the best layout is selected by finding the best match between vectors. Feasible solutions of speech balloons constitute a solution space, and the best solution that jointly describes the best locations of all balloons in a page is determined by the particle swarm optimization algorithm. Objective evaluation and subjective evaluation are designed from various perspectives to demonstrate effectiveness and superiority of the proposed system. [ABSTRACT FROM PUBLISHER]

We provide a study of the relation between the near- and far-fields in antenna systems by working with a concrete design problem. The task of finding an antenna array capable of synthesizing a desired near-field distribution is tackled within the general framework of near-field theories recently proposed in literature. We use a genetic algorithm to search for a set of small antennas by working only with far-field data. It is shown, as predicted theoretically, that such information in the far zone are sufficient for reconstructing the entire near-field in the exterior region. We provide a set of examples demonstrating the procedure and validating the proposals. The methodology can be applied to arbitrary antennas and any desired near-field pattern, and we hope it will help automating design methods in the emerging research area of near-field array synthesis. [ABSTRACT FROM PUBLISHER]

Binary sequences with low aperiodic autocorrelation levels, defined in terms of the peak sidelobe level (PSL) and/or merit factor, have many important engineering applications, such as radars, sonars, spread spectrum communications, system identification, and cryptography. Searching for low autocorrelation binary sequences (LABS) is a notorious combinatorial problem, and has been chosen to form a benchmark test for constraint solvers. Due to its prohibitively high complexity, an exhaustive search solution is impractical, except for relatively short lengths. Many suboptimal algorithms have been introduced to extend the LABS search for lengths of up to a few hundred. In this paper, we address the challenge of discovering even longer LABS by proposing an evolutionary algorithm (EA) with a new combination of several features, borrowed from genetic algorithms, evolutionary strategies (ES), and memetic algorithms. The proposed algorithm can efficiently discover long LABS of lengths up to several thousand. Record-breaking minimum peak sidelobe results of many lengths up to 4096 have been tabulated for benchmarking purposes. In addition, our algorithm design can be easily adapted to tackle various extensions of the LABS problem, say, with a generic sidelobe criterion and/or for possibly nonbinary sequences. [ABSTRACT FROM AUTHOR]

In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms. [ABSTRACT FROM PUBLISHER]

This paper deals with the development and characterization of a high frequency (HF) label-free microfluidic biosensor for the non-invasive analysis in liquid of cell intracellular properties. Thanks to the measurement of intrinsic cell dielectric properties, information on their type, pathological state or either differentiation status can be deduced. This method permits to envision promising early diagnosis tools. The presented microfluidic biosensor is based on a capacitively coupled resonators architecture made with thick gold electrodes designed to ensure a high sensitivity to cells flowing in a microfluidic channel. To prove the concept and access the detection capabilities of such sensors, HF measurements have been performed on polystyrene beads and results have been compared to full-wave electromagnetic simulations. The results demonstrate the potential of the developed method to characterize cell dielectric properties in a microfluidic environment. [ABSTRACT FROM AUTHOR]

This paper presents experimental demonstrations of size-selective parallel templated assembly of single biological cells on a polydimethylsiloxane (PDMS) template. Using the ultrasonic assembly technique known as templated assembly by selective removal (TASR), single chlorella cells with diameters between 5 and 20 μm were individually assembled in parallel with high size-based selectivity and precise alignment into their size-matching hemispherical assembly sites on the template. Optical observation of assembled single cells on the template without cell staining was enabled by transparency of the PDMS template. No significant change in the cell morphology was observed after assembly, confirming that the conditions of ultrasound used in the assembly are compatible. The presented size-selective trapping of single biological cells on the transparent and biocompatible PDMS template represents a biological tool with potential for direct implementation in various biological applications including cell sorting and isolation for optical cell diagnostics or cell templating for artificial tissue replication. [ABSTRACT FROM AUTHOR]

In molecular communication, a group of biological nanomachines communicates through exchanging molecules and collectively performs application dependent tasks. An open research issue in molecular communication is to establish interfaces to interconnect the molecular communication environment (e.g., inside the human body) and its external environment (e.g., outside the human body). Such interfaces allow conventional devices in the external environment to control the location and timing of molecular communication processes in the molecular communication environment and expand the capability of molecular communication. In this paper, we first describe an architecture of externally controllable molecular communication and introduce two types of interfaces for biological nanomachines; bio-nanomachine to bio-nanomachine interfaces (BNIs) for bio-nanomachines to interact with other biological nanomachines in the molecular communication environment, and inmessaging and outmessaging interfaces (IMIs and OMIs) for bio-nanomachines to interact with devices in the external environment. We then describe a proof-of- concept design and wet laboratory implementation of the IMI and OMI, using biological cells. We further demonstrate, through mathematical modeling and numerical experiments, how an architecture of externally controllable molecular communication with BNIs and IMIs/OMIs may apply to pattern formation, a promising nanomedical application of molecular communication. [ABSTRACT FROM PUBLISHER]

The core promoters play significant and extensive roles for the initiation and regulation of DNA transcription. The identification of core promoters is one of the most challenging problems yet. Due to the diverse nature of core promoters, the results obtained through existing computational approaches are not satisfactory. None of them considered the potential influence on performance of predictive approach resulted by the interference between neighboring TSSs in TSS clusters. In this paper, we sufficiently considered this main factor and proposed an approach to locate potential TSS clusters according to the correlation of regional profiles of DNA and TSS clusters. On this basis, we further presented a novel computational approach (ProMT) for promoter prediction using Markov chain model and predictive TSS clusters based on structural properties of DNA. Extensive experiments demonstrated that ProMT can significantly improve the predictive performance. Therefore, considering interference between neighboring TSSs is essential for a wider range of promoter prediction. [ABSTRACT FROM PUBLISHER]