published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

Το διάνυσμα προτύπων αποτελεί σύνθεση ενός συνόλου χαρακτηριστικών γνωρισμάτων και ταξινομείται σε δύο κατηγορίες: τα αρχικά χαρακτηριστικά γνωρίσματα και ένα σύνολο μη γραμμικών προβολών των αρχικών χαρακτηριστικών γνωρισμάτων. Στην ταξινόμηση, η κατασκευή χαρακτηριστικών γνωρισμάτων είναι ένα βήμα προ-επεξεργασίας στο οποίο ένα ή περισσότερα γνωρίσματα κατασκευάζονται από ένα αρχικό σύνολο. Ο αριθμός και ο τύπος των χαρακτηριστικών γνωρισμάτων είναι κρίσιμα για την ακρίβεια ταξινόμησης και την υπολογιστική πολυπλοκότητα. Καθώς ο αριθμός των χαρακτηριστικών γνωρισμάτων αυξάνει, απαιτούνται πρόσθετα παραδείγματα για να ολοκληρώσουν μια αξιόπιστη διαδικασία κατάρτισης, επιτρέποντας περισσότερες δυνατότητες αξιόπιστης γενίκευσης χωρίς υπέρ-εκπαίδευση αποτελεσμάτων. Σε αυτή τη διπλωματική εργασία εξετάζουμε και αναλύουμε τη χρήση του Γενετικού προγραμματισμού για την προ-επεξεργασία δεδομένων έτσι ώστε να κατασκευάσουμε μη γραμμικά, ιδιαίτερα προφητικά, χαρακτηριστικά γνωρίσματα από τα αρχικά. Για το σκοπό αυτό χρησιμοποιούμε γενετικούς αλγορίθμους και συγκεκριμένα τον C4.5 αλγόριθμο εκμάθησης δέντρων απόφασης, τον G-Net, ένα διανεμημένο εξελικτικό αλγόριθμο ικανό να συμπεράνει τους ταξινομητές από τα προ-συγκεντρωμένα στοιχεία καθώς και τη μέθοδο που βασίζεται στο συνδυασμό της καθιερωμένης τεχνικής της γραμματικής εξέλιξης και των τεχνητών νευρικών δικτύων. Εφαρμόζοντας τον γενετικό προγραμματισμό σε διάφορα σύνολα δεδομένων ταξινόμησης επιτυγχάνουμε μεγαλύτερη ακρίβεια ταξινόμησης. Όλοι οι αλγόριθμοι που χρησιμοποιήθηκαν έδωσαν πολύ καλύτερη απόδοση στα σύνολα δεδομένων όταν συμπεριλήφθηκε μια ενιαία εξελιγμένη μεταβλητή, επιλύοντας προβλήματα που βρίσκονται πολύ συχνά στις εφαρμογές υπολογιστών όπως Ιατρική και ελαττωματική διάγνωση, πρόγνωση, αναγνώριση εικόνας, κατηγοριοποίηση κειμένων, προσαρμοστική σκιαγράφηση χρηστών. / -

This thesis investigates the use of symbiosis as an evolutionary metaphor for problem decomposition using Genetic Programming. It begins by drawing a connection between lateral problem decomposition, in which peers with similar capabilities coordinate their actions, and vertical problem decomposition, whereby solution subcomponents are organized into increasingly complex units of organization. Furthermore, the two types of problem decomposition are associated respectively with context learning and layered learning. The thesis then proposes the Symbiotic Bid-Based framework modeled after a three-staged process of symbiosis abstracted from biological evolution. As such, it is argued, the approach has the capacity for both types of problem decomposition. Three principles capture the essence of the proposed framework. First, a bid-based approach to context learning is used to separate the issues of `what to do' and `when to do it'. Whereas the former issue refers to the problem-specific actions, e.g., class label predictions, the latter refers to a bidding behaviour that identifies a set of problem conditions. In this work, Genetic Programming is used to evolve the bids casting the method in a non-traditional role as programs no longer represent complete solutions. Second, the proposed framework relies on symbiosis as the primary mechanism of inheritance driving evolution, where this is in contrast to the crossover operator often encountered in Evolutionary Computation. Under this evolutionary metaphor, a set of symbionts, each representing a solution subcomponent in terms of a bid-action pair, is compartmentalized inside a host. Communication between symbionts is realized through their collective bidding behaviour, thus, their cooperation is directly supported by the bid-based approach to context learning. Third, assuming that challenging tasks where problem decomposition is likely to play a key role will often involve large state spaces, the proposed framework includes a dynamic evaluation function that explicitly models the interaction between candidate solutions and training cases. As such, the computational overhead incurred during training under the proposed framework does not depend on the size of the problem state space. An approach to model building, the Symbiotic Bid-Based framework is first evaluated on a set of real-world classification problems which include problems with multi-class labels, unbalanced distributions, and large attribute counts. The evaluation includes a comparison against Support Vector Machines and AdaBoost. Under temporal sequence learning, the proposed framework is evaluated on the truck reversal and Rubik's Cube tasks, and in the former case, it is compared with the Neuroevolution of Augmenting Topologies algorithm. Under both problems, it is demonstrated that the increased capacity for problem decomposition under the proposed approach results in improved performance, with solutions employing vertical problem decomposition under temporal sequence learning proving to be especially effective.

This research investigates the ability of genetic programming to build profitable trad- ing strategies for the Foreign Exchange Market (FX) of one major currency pair (EURUSD) using one hour prices from July 1, 2009 to November 30, 2012. We rec- ognize that such environments are likely to be non-stationary and we do not expect that a single training partition, used to train a trading agent, represents all likely future behaviours. The proposed adaptive retraining algorithm – hereafter FXGP – detects poor trading behaviours and trains a new trading agent. This represents a significant departure from current practice which assumes some form of continuous evolution. Extensive benchmarking is performed against the widely used EURUSD currency pair. The non-stationary nature of the task is shown to result in a prefer- ence for exploration over exploitation. Moreover, adopting a behavioural approach to detecting retraining events is more effective than assuming incremental adaptation on a continuous basis. From the application perspective, we demonstrate that use of a validation partition and Stop-Loss (S/L) orders significantly improves the perfor- mance of a trading agent. In addition the task of co-evolving of technical indicators (TI) and the decision trees (DT) for deploying trading agent is explicitly addressed. The results of 27 experiments of 100 simulations each demonstrate that FXGP sig- nificantly outperforms existing approaches and generates profitable solutions with a high probability.

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.

Grammatical Evolution (GE) is a method of utilising a general purpose evolutionary algorithm to ???evolve??? programs written in an arbitrary BNF grammar. This thesis extends GE as follows: GE as an extension of Genetic Programming (GP) A novel method of automatically extracting information from the grammar is introduced. This additional information allows the use of GP style crossover which in turn allows GE to perform identically to a strongly typed GP system as well as a non-typed (or canonical) GP system. Two test problems are presented one which is more easily solved by the GP style crossover and one which favours the tradition GE ???Ripple Crossover???. With this new crossover operator GE can now emulate GP (as well as retaining its own unique features) and can therefore now be seen as an extension of GP. Dynamically Defined Functions An extension to the BNF grammar is presented which allows the use of dynamically defined functions (DDFs). DDFs provide an alternative to the traditional approach of Automatically Defined Functions (ADFs) but have the advantage that the number of functions and their parameters do not need to be specified by the user in advance. In addition DDFs allow the architecture of individuals to change dynamically throughout the course of the run without requiring the introduction of any new form of operator. Experimental results are presented confirming the effectiveness of DDFs. Self-Selecting (or variable) crossover. A self-selecting operator is introduced which allows the system to determine, during the course of the run, which crossover operator to apply; this is tested over several problem domains and (especially where small populations are used) is shown to be effective in aiding the system to overcome local optima. Spatial Co-Evolution in Age Layered Planes (SCALP) A method of combining Hornby???s ALPS metaheuristic and a spatial co-evolution system used by Mitchell is presented; the new SCALP system is tested over three problem domains of increasing difficulty and performs extremely well in each of them.

Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references.

Thesis (MScIng)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Thesis (M.C.S.)--Carleton University, 2004. / Includes bibliographical references (p. 84-91). Also available in electronic format on the Internet.

Thesis (M.Sc.)--Victoria University of Wellington, 2008. / Includes bibliographical references.