published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

In recent years technical trading rules are widely known by more and more people, not only the academics many investors also learn to apply them in financial markets. One approach of constructing technical trading rules is to use technical indicators, such as moving average(MA) and filter rules. These trading rules are widely used possibly because the technical indicators are simple to compute and can be programmed easily. An alternative approach of constructing technical trading rules is to rely on some chart patterns. However, the patterns and signals detected by these rules are often made by the visual inspection through human eyes. As for as I know, there are no universally acceptable methods of constructing the chart patterns. In 2000, Prof. Andrew Lo and his colleagues are the first ones who define five pairs of chart patterns mathematically. They are Head-and-Shoulders(HS) & Inverted Headand- Shoulders(IHS), Broadening tops(BTOP) & bottoms(BBOT), Triangle tops(TTOP) & bottoms(TBOT), Rectangle tops(RTOP) & bottoms( RBOT) and Double tops(DTOP) & bottoms(DBOT). The basic formulation of a chart pattern consists of two steps: detection of (i) extreme points of a price series; and (ii) shape of the pattern. In Lo et al.(2000), the method of kernel smoothing was used to identify the extreme points. It was admitted by Lo et al. (2000) that the optimal bandwidth used in kernel method is not the best choice and the expert judgement is needed in detecting the bandwidth. In addition, their work considered chart pattern detection only but no buy/sell signal detection. It should be noted that it is possible to have a chart pattern formed without a signal detected, but in this case no transaction will be made. In this thesis, I propose a new class of technical trading rules which aims to resolve the above problems. More specifically, each chart pattern is parameterized by a set of parameters which governs the shape of the pattern, the entry and exit signals of trades. Then the optimal set of parameters can be determined by using genetic algorithms (GAs). The advantage of GA is that they can deal with a high-dimensional optimization problems no matter the parameters to be optimized are continuous or discrete. In addition, GA can also be convenient to use in the situation that the fitness function is not differentiable or has a multi-modal surface. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

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published_or_final_version / abstract / toc / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / toc / Mechanical Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

Στη σύχρονη εποχή η ανάπτυξη των ετερογενών και κατανεμημένων περιβαλλόντων, όπως τα περιβάλλοντα πλέγματος, καθιστά εφικτή την επίλυση υπολογιστικά εντατικών προβλημάτων με αξιόπιστο και οικονομικό τρόπο. Το Πλέγμα υπολογιστών είναι μια αναπτυσσόμενη δομή που παρέχει πρόσβαση σε υπολογιστική ισχύ και αποθηκευτικό χώρο κατανεμημένα σε όλο τον κόσμο και εισήχθη για να ικανοποιήσει την ανάγκη για εφαρμογές που απαιτούν μεγάλο αριθμό υπολογισμών καθώς και την επικοινωνία των ατόμων που τις εκτελούν. Στην παρούσα διπλωματική εργασία παρουσίαζεται με λεπτομέρεια η δομή και ο τρόπος λειτουργίας και εξυπηρέτησης χρηστών του Πλέγματος. Επικεντρώνουμε το ενδιαφέρον μας στη μελέτη εκτέλεσης αλγορίθμων στο Πλέγμα προσφέροντας στον αναγνώστη τη δυνατότητα εξοικείωσης με τις ιδιαιτερότητες συγγραφής αλγορίθμων και εφαρμογών που επιθυμούν να εκμεταλλευτούν τις δυνατότητες του Πλέγματος. Επίσης, καταγράφουμε τη μεθοδολογία και τον τρόπο υποβολής εργασιών στο Πλέγμα από τη σκοπιά του χρήστη. Από τους αλγορίθμους που καταγράφονται, ιδιαίτερη έμφαση δόθηκε στις παράλληλες εφαρμογές που εκμεταλλεύονται το μεγάλο πλεονέκτημα που μας προσφέρει το Πλέγμα εφοδιάζοντας μας με πολλαπλές επεξεργαστικές μονάδες. Συγκεκριμένα για να εξετάσουμε τόσο την αρχιτεκτονική όσο και τις δυνατότητες του Πλέγματος, αναπτύξαμε και συγγράψαμε ένα παράλληλο Γενετικό Αλγόριθμο για το πολυδίαστατο και απαιτητικό πρόβλημα του Χρονοπρογραμματισμού Εξετάσεων σε Πανεπιστήμια. / In our contemporary society, the development of distributed and heterogeneous computing environments, provide us with the capability of solving difficult scientific problems in a reliable and economic manner. The Grid environment is a form of distributed computing whereby a “super and virtual computer” is composed of a cluster of networked loosely coupled computers acting in concert to perform very large tasks. This technology has been applied to computationally intensive scientific, mathematical, and academic problems through volunteer computing, and it is used in commercial enterprises for such diverse applications as drug discovery, economic forecasting, seismic analysis, and back-office data processing in support of e-commerce and Web services. In the current paper, there is a significant reference to the architecture and the different elements of which Grid is composed .We focus our interest in the way we should write and develop our algorithms in order to retrieve the benefits of Grid computing. As far as the user aspect is concerned, we describe the methodology of job submission and job monitoring in Grid. From the algorithms we developed and tested, there is a significant emphasis concerning the parallel applications which take advantage of the fact that Grid offers us multiple computing resources and processors. In detail, we developed a parallel genetic algorithm for the examination timetabling problem in Universities which consists a difficult np problem which provides a significant example of the importance and the role of Grid in Computational Science.

The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many sub-systems which may be present in such a robot, one of which is path planning — the ability to determine a sequence of positions or configurations between an initial and goal position within a particular obstacle cluttered workspace. Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years, evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by successful developments in hardware. These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem areas. This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of future path planning techniques.