This thesis studied a new variant of Evolutionary Algorithms called Gene Expression Programming. The evolution process of Gene Expression Programming was investigated from the practice to the theory. As a practice level, the original version of Gene Expression Programming was applied to a classification problem and an enhanced version of the algorithm was consequently developed. This allowed the development of a general understanding of each component of the genotype and phenotype separated representation system of the solution employed by the algorithm. Based on such an understanding, a version of the schema theory was developed for Gene Expression Programming. The genetic modifications provided by each genetic operator employed by this algorithm were analysed and a set of theorems predicting the propagation of the schema from one generation to another was developed. Also a set of experiments were performed to test the validity of the developed schema theory obtaining good agreement between the experimental results and the theoretical predictions.

Yes / Manning’s roughness coefficient (n) has been widely used to estimate flood discharges and flow depths in natural channels. Therefore, although extensive guidelines are available, the selection of the appropriate n value is of great importance to hydraulic engineers and hydrologists. Generally, the largest source of error in post-flood estimates is caused by the estimation of n values, particularly when there has been minimal field verification of flow resistance. This emphasizes the need to improve methods for evaluating the roughness coefficients. Trinidad and Tobago currently does not have any set method or standardised procedure that they use to determine the n value. Therefore, the objective of this study was to develop a soft computing model in the calculation of the roughness coefficient values using low flow discharge measurements for a stream. This study presents Gene-Expression Programming (GEP), as an improved approach to compute Manning’s Roughness Coefficient. The GEP model was found to be accurate, producing a coefficient of determination (R2) of 0.94 and Root Mean Square Error (RSME) of 0.0024.

Big data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.

In the thesis, we apply the gene expression programming (GEP) to training profitable trading strategies. We propose a model which utilizes several historical periods that are highly related to the current template period, and the best trading strategies of the historical periods generate the trading signals. To keep stability of our model, we proposed the trading decision mechanism based on simple majority vote in our model. The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) is selected as our investment target and the trading period starts from 2000/9/14 to 2012/1/17, approximately twelve years. In our experiments, the lengths of our training period are 60, 90, 120, 180, and 270 trading days, respectively. We observe that the model with higher voting threshold usually can make profitable trading decisions. The best cumulative return 236.25\% and the best annualized cumulative return 10.63\% occur when the 180-day training models pairs with available threshold 0.21 and voting threshold 0.88, which are higher than the cumulative return 0.96\% and annualized cumulative return 0.08\% of the buy-and-hold strategy.

Τo Gene Expression Programming - GEP (Προγραμματισμός Γονιδιακής Έκφρασης - ΠΓΕ) είναι μια μέθοδος αυτόματης παραγωγής προγραμμάτων η οποία ανήκει στη γενική κατηγορία των Εξελικτικών Αλγορίθμων, εκείνων των τεχνικών δηλαδή που εμπνέονται από τις φυσικές διεργασίες της βιολογικής εξέλιξης. Συγκεκριμένα ο ΠΓΕ χρησιμοποιεί πληθυσμούς από άτομα, επιλέγει τα άτομα σύμφωνα με την καταλληλότητά τους (fitness) και εισάγει νέα σημεία (άτομα, πιθανές λύσεις) στον πληθυσμό χρησιμοποιώντας έναν ή περισσότερους γενετικούς τελεστές. Στόχος αυτής της Μεταπτυχιακής Διπλωματικής Εργασίας ήταν ο σχεδιασμός και η υλοποίηση ενός Ολοκληρωμένου Γραφικού Περιβάλλοντος για τον Προγραμματισμό Γονιδιακής Έκφρασης καθώς και η υλοποίηση ορισμένων καινοτομιών. Στα πλαίσια της διπλωματικής εργασίας, σχεδιάσθηκε και αναπτύχθηκε ένας καινοτόμος τελεστής για την μέθοδο του ΠΓΕ. Ο συγκεκριμένος τελεστής πραγματοποιεί μια τοπική αναζήτηση στις μεταβλητές που χρησιμοποιούνται στη μοντελοποίηση του εκάστοτε προβλήματος και επιλέγει εκείνες τις μεταβλητές για τις οποίες η απόδοση του αλγορίθμου βελτιστοποιείται. Η απόδοση του καινούργιου τελεστή ελέγχθηκε και πειραματικά. Μια επιπλέον καινοτομία που εφαρμόστηκε είναι η αυξομείωση του αριθμού των μεταλλάξεων. Συγκεκριμένα, επιλέγουμε να μειώνουμε τον αριθμό των μεταλλάξεων καθώς ο πληθυσμός εξελίσσεται, ενώ τον αυξάνουμε όταν έχουμε μικρή διαφορά ανάμεσα στη βέλτιστη και τη μέση απόδοση του πληθυσμού. Ο μεταβλητός αριθμός μεταλλάξεων σε συνδυασμό με την ικανότητα της μεθοδολογίας του ΠΓΕ να αποφεύγει τα τοπικά ακρότατα βελτιώνει σημαντικά την προσαρμοστικότητα του αλγορίθμου. Επιπλέον, για την αντιμετώπιση της αυξημένης υπολογιστικής πολυπλοκότητας που παρουσιάζει η μέθοδος, εισήχθη η έννοια του παραλληλισμού. Τέλος, η τροποποιημένη μέθοδος του ΠΓΕ εφαρμόστηκε σε πληθώρα προβλημάτων όπως η μοντελοποίηση συμπεριφοράς μιας χρονοσειράς μαγνητοεγκεφαλογραφήματος, η μοντελοποίηση της συμπεριφοράς κόπωσης υλικών, η πρόβλεψη ισοτιμίας δολαρίου – ευρώ, η πρόβλεψη πρωτεϊνικών αλληλεπιδράσεων και η πρόβλεψη του βαθμού υδατοκορεσμού ελαιοκαλλιεργειών. Τα αποτελέσματα που προέκυψαν είναι ιδιαίτερα ενθαρρυντικά. / Gene Expression Programming (GEP) is one method of automatic generation of programs that belongs to a wider class of Evolutionary Algorithms. Evolutionary Algorithms are inspired by biological mechanisms of evolution. Specifically, GEP uses populations of individuals, select the individuals according to their fitness, and introduce genetic variation using one or more genetic operators. The purpose of this Master's Thesis was to design and implement an Integrated Graphical Environment for Gene Expression Programming and the implementation of certain innovations. Ιn the context of this thesis an innovative operator was designed and developed for the GEP method. This particular operator is conducting a local search on the variables used in modeling of a problem and chooses those variables for which the performance of the algorithm is optimized. The performance of the new operator was experimentally tested. Another innovation implemented was the fluctuation in the number of mutations. Specifically, we choose to reduce the number of mutations as the population evolves, while we increase it when the performance of the best individual found is very close to the average performance of the population. The variable number of mutations in combination with the ability of the methodology of GEP to avoid local extrema significantly improves the adaptability of the algorithm. Moreover, in order to face the increased computational complexity of the method, we introduce parallelism. Finally, the modified method of GEP was applied to many problems such as modeling behavior of a MEG’s time series, modeling of fatigue behavior of materials, forecasting Euro - United States Dollar exchange rate, predicting protein interactions and predicting the degree of saturation of olive crops. The results are very encouraging.

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Finding an optimal solution for the logic circuit design problem is challenging and time-consuming especially for complex logic circuits. As the number of logic gates increases the task of designing optimal logic circuits extends beyond human capability. A number of evolutionary algorithms have been invented to tackle a range of optimisation problems, including logic circuit design. This dissertation explores two of these evolutionary algorithms i.e. Gene Expression Programming (GEP) and Multi Expression Programming (MEP) with the aim of integrating their strengths into a new Genetic Programming (GP) algorithm. GEP was invented by Candida Ferreira in 1999 and published in 2001 [8]. The GEP algorithm inherits the advantages of the Genetic Algorithm (GA) and GP, and it uses a simple encoding method to solve complex problems [6, 32]. While GEP emerged as powerful due to its simplicity in implementation and exibility in genetic operations, it is not without weaknesses. Some of these inherent weaknesses are discussed in [1, 6, 21]. Like GEP, MEP is a GP-variant that uses linear chromosomes of xed length [23]. A unique feature of MEP is its ability to store multiple solutions of a problem in a single chromosome. MEP also has an ability to implement code-reuse which is achieved through its representation which allow multiple references to a single sub-structure. This dissertation proposes a new GP algorithm, Improved Gene Expression Programming (IGEP) which im- proves the performance of the traditional GEP by combining the code-reuse capability and simplicity of gene encoding method from MEP and GEP, respectively. The results obtained using the IGEP and the traditional GEP show that the two algorithms are comparable in terms of the success rate when applied on simple problems such as basic logic functions. However, for complex problems such as one-bit Full Adder (FA) and AND-OR Arithmetic Logic Unit (ALU) the IGEP performs better than the traditional GEP due to the code-reuse in IGEP / Mathematical Sciences / M. Sc. (Applied Mathematics)