In polymerase chain reaction (PCR), in order to amplify massive DNA sequences successfully, it needs to design an appropriate primer pair. The constraints derived from the traits of PCR for proceeding PCR are used in searching for primer pairs. In this paper, in order to decrease the searching space and to increase the feasible quality of primers, a double orthogonal arrays intelligent crossover genetic algorithm (DOAIGA) is used to solve the primer design problem. DOAIGA combines the traditional genetic algorithm and the Taguchi methodology to efficiently search feasible primers under required constraints. The proposed intelligent crossover subsystem mainly concentrates on the better genes more systematic. The key point of DOAIGA is to achieve the elitism goal by applying the orthogonal arrays (OAs) that is used in quality engineering with a small amount of experiment features. In this thesis, the double orthogonal arrays are used to approach a better forward and reverse primers separately. Compared to the current existing softwares, DOAIGA can obtain feasible primer pairs more effectively. Finally the correctness of primer pair is verified by PCR experiment.

The architectures of the control devices in active control algorithm are an important fact in civil structural buildings. Traditional research has limitations in finding the optimal architecture of control devices such as using predefined numbers or locations of sensors and dampers within the 2-and 3-dimensional (3-D) model of the structure. Previous research using single-objective optimization only provides limited data for defining the architecture of sensors and control devices. The Linear Quadratic Gaussian (LQG) control algorithm is used as the active control strategy. The American Society of Civil Engineers (ASCE) control benchmark building definition is used to develop the building system model. The proposed gene manipulation genetic algorithm (GMGA) determines the near-optimal Pareto fronts which consist of varying numbers and locations of sensors and control devices for controlling the ASCE benchmark building by considering multi-objectives such as interstory drift and minimizing the number of the control devices. The proposed GMGA reduced the central processing unit (CPU) run time and produced more optimal Pareto fronts for the 2-D and 3-D 20-story building models. Using the GMGA provided several benefits: (1) the possibility to apply any presuggested multi-objective optimization mechanism; (2) the availability to perform a objective optimization problem; (3) the adoptability of the diverse encoding provided by the GA; (4) the possibility of including the engineering judgment in generating the next generation population by using a gene creation mechanisms; and (5) the flexibility of the gene creation mechanism in applying and changing the mechanism dependent on optimization problem. The near-optimal Pareto fronts obtained offer the structural engineer a diverse choice in designing control system and installing the control devices. The locations and numbers of the dampers and sensors in each story are highly dependent on the sensor locations. By providing near-Pareto fronts of possible solutions to the engineer that also consider diverse earthquakes, the engineer can get normalized patterns of architectures of control devices and sensors about random earthquakes.

In this thesis, we propose an investment strategy for the world mutual funds. We first define the global trend indicator (GTI) for evaluating the price change trend of the funds in the future. Then, based on GTI, we derive the monitoring indicator (MI) to measure whether the fund market is in the bull or bear state. MI decides the buying or selling signal. The goodness of a fund is determined by some weighted combination of short-term performance and long-term performance. The weights of various performances are decided by the genetic algorithm, which can dynamically adjusted with priorities of interested funds according to their past performances (profitability). When a buying signal is triggered by MI, the funds with high performance are more likely to be picked than those with low performance. In our experimental results from January 1999 to December 2008 (10 years in total), we achieve the annual profit higher than 10%, which is a significant improvement to other existing methods.

The problem of dynamic stability of power system has challenged power system engineers since over three decades now. In a generator, the electromechanical coupling between the rotor and the rest of the system causes it to behave in a manner similar to a spring mass damper system, which exhibits an oscillatory behaviour around the equilibrium state, following any disturbance, such as sudden change in loads, change in transmission line parameters, fluctuations in the output of turbine and faults etc. The use of fast acting high gain AVRs and evolution of large interconnected power systems with transfer of bulk power across weak transmission links have further aggravated the problem of low frequency oscillations. The oscillations, which are typically in the frequency range of 0.2 to 3.0 Hz, might be excited by the disturbances in the system or, in some cases, might even build up spontaneously. These oscillations limit the power transmission capability of a network and, sometimes, even cause a loss of synchronism and an eventual breakdown of the entire system. The application of Power System Stabilizer (PSS) can help in damping out these oscillations and improve the system stability. The traditional and till date the most popular solution to this problem is application of conventional power system stabilizer (CPSS). However, continual changes in the operating condition and network parameters result in corresponding change in system dynamics. This constantly changing nature of power system makes the design of CPSS a difficult task. Adaptive control methods have been applied to overcome this problem with some degree of success. However, the complications involved in implementing such controllers have restricted their practical usage. In recent years there has been a growing interest in robust stabilization and disturbance attenuation problem. H∞ control theory provides a powerful tool to deal with robust stabilization and disturbance attenuation problem. However the standard H∞ control theory does not guarantee robust performance under the presence of all the uncertainties in the power plants. This thesis provides a method for designing fixed parameter controller for system to ensure robustness under model uncertainties. Minimum performance required of PSS is decided a priori and achieved over the entire range of operating conditions. A new method has been proposed for tuning the parameters of a fixed gain power system stabilizer. The stabilizer places the troublesome system modes in an acceptable region in the complex plane and guarantees a robust performance over a wide range of operating conditions. Robust D-stability is taken as primary specification for design. Conventional lead/lag PSS structure is retained but its parameters are re-tuned using genetic algorithm (GA) to obtain enhanced performance. The advantage of GA technique for tuning the PSS parameters is that it is independent of the complexity of the performance index considered. It suffices to specify an appropriate objective function and to place finite bounds on the optimized parameters. The efficacy of the proposed method has been tested on single machine as well as multimachine systems. The proposed method of tuning the PSS is an attractive alternative to conventional fixed gain stabilizer design as it retains the simplicity of the conventional PSS and still guarantees a robust acceptable performance over a wide range of operating and system condition. The method suggested in this thesis can be used for designing robust power system stabilizers for guaranteeing the required closed loop performance over a prespecified range of operating and system conditions. The simplicity in design and implementation of the proposed stabilizers makes them better suited for practical applications in real plants.

The paper "Parallel Genetic Algorithms" discusses the theoretical basics of Evolutionary Algorithms concentrating on Genetic Algorithms. Possibilities for a parallelization of these algorithms are examined and explained on the basis of concepts of parallel programming. A concrete suggestion for a practical realization of a parallel Genetic Algorithm at different levels of complexity is presented. / Die Studienarbeit zum Thema "Parallele Genetische Algorithmen" befasst sich mit den theoretischen Grundlagen Evolutionärer Algorithmen, wobei die Konzentration bei Genetischen Algorithmen liegt, und untersucht die Möglichkeiten einer parallelen Realisierung dieser Algorithmen. Des weiteren werden Konzepte der Parallelen Programmierung diskutiert sowie ein konkreter Vorschlag zur praktischen Realisierung eines parallelen Genetischen Algorithmus' auf verschiedenen Komplexitätsebenen vorgestellt.

Quantifying and characterizing isolated tumor cells (ITCs) is of interest in surgical pathology and cytology for its potential to provide data for cancer staging, classification, and treatment. Although the independent prognostic significance of circulating ITCs has not been proven, their presence is gaining clinical relevance as an indicator. However, researchers have not established an optimal method for detecting ITCs. Consequently, this Ph.D. dissertation is concerned with the development and evaluation of dielectric spectroscopy as a low-cost method for cell characterization and quantification. In support of this goal, ultra-wideband (UWB), microwave pulse generator circuits, coaxial transmission line fixtures, permittivity extraction algorithms, and dielectric spectroscopy measurement systems were developed for evaluating the capacity to quantify B16-F10 tumor cells in suspension. First, this research addressed challenges in developing tunable UWB circuits for pulse generation. In time-domain dielectric spectroscopy, a tunable UWB pulse generator facilitates exploration of microscopic dielectric mechanisms, which contribute to dispersion characteristics. Conventional approaches to tunable pulse generator design have resulted in complex circuit topologies and unsymmetrical waveform morphologies. In this research, a new design approach for low-complexity, tunable, sub-nanosecond and UWB pulse generator was developed. This approach was applied to the development of a novel generator that produces symmetrical waveforms (patent pending 60/597,746). Next, this research addressed problems with transmission-reflection (T/R) measurement of cell suspensions. In T/R measurement, coaxial transmission line fixtures have historically required an elaborate sample holder for containing liquids, resulting in high cost and complexity. Furthermore, the algorithms used to extract T/R dielectric properties have suffered from myriad problems including local minima and half-wavelength resonance. In this dissertation, a simple coaxial transmission line fixture for holding liquids by dispensing with the air-core assumption inherent in previous designs was developed (patent pending 60/916,042). In addition, a genetic algorithm was applied towards extracting dielectric properties from measurement data to circumvent problems of local minima and half wavelength resonance. Finally, in this research the capacity for using dielectric properties to quantify isolated B16-F10 tumor cells in McCoy's liquid medium was investigated. In so doing, the utility of the Maxwell-Wagner mixture formula for cell quantification was demonstrated by measuring distinct dielectric properties for differing volumes of cell suspensions using frequency- and time-domain dielectric spectroscopy.

This thesis explores a border security system based on agent-based modeling and simulation (ABMS). The ABMS software platform, map aware non-uniform automata, is used to model various scenarios and evaluate the border security system given a set of infiltrators who have evolutionary behavior governed by a genetic algorithm (GA). The GA is used to represent adaptive behavior of the enemy when the friendly force has deployed our border security at a maximum level. By using a near optimal Latin hypercube design, our simulation runs are implemented efficiently and the border security system is analyzed using four different kinds of measures of effectiveness. / text

Three related electromagnetic scattering problems, namely, high frequency electromagnetic (EM) ray tracing, scattering feature extraction, and inverse scattering are studied in this dissertation. New approaches are presented to advance the state of the art in each of the areas. The presented study in electromagnetic ray tracing leads to an alternative ray tracing algorithm which can outperform the traditional algorithms for complex targets. The performance of the proposed techniques demonstrates their potential application to the study of high-frequency EM scattering prediction. Second, a genetic algorithm (GA)-based algorithm with an adaptive-feeding technique is developed to simultaneously extract both scattering centers and resonances. Scattering feature extraction algorithms are then developed with the consideration of the visibility of scattering centers. Inverse scattering problems with strong multiple scattering effects are also studied. A GA-based method is presented to invert the shapes with multiple scattering effects. An approach combining hybrid GA with the tabu list idea are then developed to further improve the performance of the GA-based inversion algorithms. / text

Model based fault detection techniques utilize functional redundancies in the static and dynamic relationships among system inputs and outputs for fault detection and isolation. Analytical models based on the underlying physics of the system can capture the dependencies between different measured signals in terms of system states and parameters. These physical models of the system can be used as a tool to detect and isolate system faults. As a machine degrades, system outputs deviate from desired outputs, generating residuals defined by the error between sensor measurements and corresponding model simulated signals. These error residuals contain valuable information to interpret system states and parameters. Setting up the measurements from a faulty system as baseline, the parameters of the idealistic model can be varied to minimize these residuals. This process is called “Parameter Tuning”. A framework to automate this “Parameter Tuning” process is presented with a focus on DC motors and 3-phase induction motors. The parameter tuning module presented is a multi-tier module which is designed to operate on real system models that are highly non-linear. The tuning module combines artificial intelligence techniques like Quasi-Monte Carlo (QMC) sampling (Hammersley sequencing) and Genetic Algorithm (Non Dominated Sorting Genetic Algorithm) with an Extended Kalman filter (EKF), which utilizes the system dynamics information available via the physical models of the system. A tentative Graphical User Interface (GUI) was developed to simplify the interaction between a machine operator and the module. The tuning module was tested with real measurements from a DC motor. A simulation study was performed on a 3-phase induction motor by suitably adjusting parameters in an analytical model. The QMC sampling and genetic algorithm stages worked well even on measurement data with the system operating in steady state condition. But the downside was computational expense and inability to estimate the parameters online – ‘batch estimator’. The EKF module enabled online estimation where update was made based on incoming measurements. But observability of the system based on incoming measurements posed a major challenge while dealing with state estimation filters. Implementation details and results are included with plots comparing real and faulty systems. / text

Τα συστήματα διαχείρισης οδοστρωμάτων χρησιμοποιούνται ευρύτατα από τους φορείς οδοποιίας και συνεχώς βελτιώνονται επειδή μπορούν να οδηγήσουν σε σημαντική εξοικονόμηση κεφαλαίου και σε υψηλά επίπεδα εξυπηρέτησης του οδικού δικτύου. Στόχος τους είναι η αξιολόγηση των αποφάσεων συντήρησης και διαχείρισης των οδοστρωμάτων για την αποτελεσματική κατανομή των περιορισμένων διαθέσιμων πόρων. Τα σύγχρονα συστήματα που έχουν αναπτυχθεί χρησιμοποιούν μεθόδους τεχνητής νοημοσύνης για την επίλυση του προβλήματος αφού αποτελούν ισχυρά εργαλεία βελτιστοποίησης με μεγάλες υπολογιστικές ικανότητες. Η πλειοψηφία των συστημάτων που έχουν δημιουργηθεί εντοπίζει τη βέλτιστη λύση που ελαχιστοποιεί το κόστος συντήρησης χωρίς να λαμβάνεται υπόψη το αντίκτυπο της επιδείνωσης της κατάστασης του οδοστρώματος στο χρήστη και στο περιβάλλον. Ωστόσο, τα σύγχρονα οδικά έργα είναι αναγκαίο να χαρακτηρίζονται από βιωσιμότητα που απαιτεί την ελαχιστοποίηση των αρνητικών επιπτώσεων που οφείλονται στην κατάσταση του οδοστρώματος. Στόχος της παρούσας μεταπτυχιακής εργασίας είναι η ανάπτυξη ενός συστήματος διαχείρισης οδοστρωμάτων το οποίο αποσκοπεί στην παροχή υποστήριξης αποφάσεων για τις κατάλληλες στρατηγικές συντήρησης ώστε να εξασφαλιστούν αποδεκτά επίπεδα ασφάλειας και λειτουργικότητας του δικτύου των αυτοκινητοδρόμων στη διάρκεια του χρόνου καθώς και μείωση των περιβαλλοντικών επιπτώσεων. Το μοντέλο που παρουσιάζεται δεν περιορίζεται μόνο στο κόστος συντήρησης, όπως οι περισσότερες μελέτες, αλλά επικεντρώνεται στο γενικευμένο κόστος που περιλαμβάνει το κόστος συντήρησης, το κόστος χρήστη και το περιβαλλοντικό κόστος. Το κόστος χρήστη αποτελείται από το κόστος λειτουργίας οχήματος, το κόστος μετακίνησης και το κόστος ατυχημάτων. Το κόστος του περιβάλλοντος συνίσταται από το κόστος εκπομπής των αέριων ρύπων, το κόστος θορύβου και το κόστος στην οικονομική, κοινωνική και πολιτική ζωή. Τα δεδομένα του προβλήματος σχετίζονται με την αρχική κατάσταση των οδοστρωμάτων που πρόκειται να συντηρηθούν, το είδος και τα χαρακτηριστικά της οδού, το είδος και τα χαρακτηριστικά των οχημάτων, τα διαθέσιμα είδη συντήρησης και το ύψος της χρηματοδότησης. Εξαιτίας του μεγέθους και της πολυπλοκότητας του προβλήματος, η βελτιστοποίηση πραγματοποιήθηκε με την εφαρμογή ενός γενετικού αλγορίθμου που έχει τη δυνατότητα να εξετάσει πλήθος οδικών τμημάτων αναζητώντας ένα ευρύ φάσμα πιθανών λύσεων μέσα σε ένα εύλογο χρονικό διάστημα υπολογισμού. Ο αλγόριθμος καταφέρνει να εντοπίζει τον κατάλληλο συνδυασμό συντηρήσεων ώστε το γενικευμένο κόστος να ελαχιστοποιείται ενώ ταυτόχρονα το οδόστρωμα να διατηρείται σε καλή κατάσταση. Για την αξιολόγηση του μοντέλου ερευνήθηκαν πολλές διαφορετικές περιπτώσεις ενώ στο τέλος σχεδιάστηκε η καμπύλη Pareto. Τα αποτελέσματα απέδειξαν ότι το προτεινόμενο σύστημα μπορεί να βοηθήσει αποτελεσματικά στη διατήρηση των οδοστρωμάτων σε ικανοποιητικά επίπεδα λειτουργικότητας και κόστους μέσω των αποφάσεων συντήρησης που προτείνει. / Pavement management systems are widely used by road agencies and are improved continuously as they can lead to money savings and high levels of road services. The aim is to provide assistance to decision makers for selecting optimum strategies in the design, evaluation, and maintenance of pavements in order to maintain them in serviceable condition over a given period of time for the least cost. Nowadays, the developed systems use artificial intelligent methods to solve the problem which are powerful optimization tools with large computational abilities. The majority of pavement management systems detect the optimal solution that minimizes the maintenance cost without considering the impact of pavement deterioration in the user and the environment. However, they should be characterized by sustainability that requires an efficient use of resources and sensitivity to environmental and social constraints. In this paper, an optimisation model is developed that aims to provide decision support to engineers in developing appropriate pavement maintenance strategies to ensure acceptable levels of safety and functionalism of the highway network in time as well as reduction of environmental impacts. The model supersedes previous ones in considering the generalised cost, which includes the agency cost, the user cost, and the environmental impacts, as the main decision parameter. The user cost consists of three main components, the vehicle operation cost, the travel cost and the accident cost. The environmental cost consists of the air pollution cost and the noise cost. The input data are related to the initial pavement condition, the type and characteristics of the road, the type and characteristics of vehicles, the maintenance types and the budget limit. Due to the problem size and complexity, the optimisation is done with the employment of a genetic algorithm which can handle a large number of road sections, search a wide range of possible solutions, and reach a solution within reasonable computation time. The genetic algorithm can find the appropriate maintenance types in order to minimize the generalized cost while the pavement is kept in good condition. The model has been evaluated with several test cases and Pareto curves have been developed. The results indicate that the proposed model can effectively assist pavement preservation and management decisions in highway networks.