Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Best Practices of Sport For Development: A Case Study of An African Organization

Sport-for-development is the active practice of achieving social ideals through the use of sport and other traditional development programs. The purpose of this thesis was to evaluate SFD best practices from the context of an African organization development project. The case was a development organization in Zambia, Africa that was utilizing sport within its strategy. The data collection and analysis framed using Curado and Bontis (2007) MIC Matrix, the Sport For Development International Working Group’s (2007) best practices model, and B. Kidd’s (2011) Sport-in-Development Logic Model. The research supports that a SFD project is multi-faceted and should include the employment of strategic community programming on the basis of collaborative and integrative sport, health care and education. Further, the researcher found that the best practices include setting specific goals and objectives, as well as instituting regular monitoring and evaluation strategies

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Methodology in Economics: the Logic of Appraisal

This Paper Intends to Develop a Coherent Methodological Framework Concerned with the Appraisal of Scientific Theories in Economics, and Which Is Based on a Postulated Aim of Science. We First Define the Scope of a Methodological Inquiry (Precise Definition of What Is Meant by the Logic of Appraisal of Scientific Theories) and Review the Work of Popper and Lakatos in the Philosophy of Science. We Then Use Their Results to Develop a Rational Structure of Scientific Activity. We Identify and Analyse Both a Micro and Macro Framework for the Process of Appraisal and Single Out the Importance of So-Called 'Fundamental Assumptions' in Creating Externalities in the Appraisal Process Which Forces Us to Adop a Multi-Level Analysis. Special Attention Is Given to the Role and Significance of the Abstraction Process and the Use of Assumptions in General. the Proposed Structure of Scientific Activity Is Illustrated with Examples From Economics.

Bounding of switching activity in logic circuits

Thèse diffusée initialement dans le cadre d'un projet pilote des Presses de l'Université de Montréal/Centre d'édition numérique UdeM (1997-2008) avec l'autorisation de l'auteur.

A Categorical Framework for the Specification and the Verification of Aspect Oriented Systems

Un objectif principal du génie logiciel est de pouvoir produire des logiciels complexes, de grande taille et fiables en un temps raisonnable. La technologie orientée objet (OO) a fourni de bons concepts et des techniques de modélisation et de programmation qui ont permis de développer des applications complexes tant dans le monde académique que dans le monde industriel. Cette expérience a cependant permis de découvrir les faiblesses du paradigme objet (par exemples, la dispersion de code et le problème de traçabilité). La programmation orientée aspect (OA) apporte une solution simple aux limitations de la programmation OO, telle que le problème des préoccupations transversales. Ces préoccupations transversales se traduisent par la dispersion du même code dans plusieurs modules du système ou l’emmêlement de plusieurs morceaux de code dans un même module. Cette nouvelle méthode de programmer permet d’implémenter chaque problématique indépendamment des autres, puis de les assembler selon des règles bien définies. La programmation OA promet donc une meilleure productivité, une meilleure réutilisation du code et une meilleure adaptation du code aux changements. Très vite, cette nouvelle façon de faire s’est vue s’étendre sur tout le processus de développement de logiciel en ayant pour but de préserver la modularité et la traçabilité, qui sont deux propriétés importantes des logiciels de bonne qualité. Cependant, la technologie OA présente de nombreux défis. Le raisonnement, la spécification, et la vérification des programmes OA présentent des difficultés d’autant plus que ces programmes évoluent dans le temps. Par conséquent, le raisonnement modulaire de ces programmes est requis sinon ils nécessiteraient d’être réexaminés au complet chaque fois qu’un composant est changé ou ajouté. Il est cependant bien connu dans la littérature que le raisonnement modulaire sur les programmes OA est difficile vu que les aspects appliqués changent souvent le comportement de leurs composantes de base [47]. Ces mêmes difficultés sont présentes au niveau des phases de spécification et de vérification du processus de développement des logiciels. Au meilleur de nos connaissances, la spécification modulaire et la vérification modulaire sont faiblement couvertes et constituent un champ de recherche très intéressant. De même, les interactions entre aspects est un sérieux problème dans la communauté des aspects. Pour faire face à ces problèmes, nous avons choisi d’utiliser la théorie des catégories et les techniques des spécifications algébriques. Pour apporter une solution aux problèmes ci-dessus cités, nous avons utilisé les travaux de Wiels [110] et d’autres contributions telles que celles décrites dans le livre [25]. Nous supposons que le système en développement est déjà décomposé en aspects et classes. La première contribution de notre thèse est l’extension des techniques des spécifications algébriques à la notion d’aspect. Deuxièmement, nous avons défini une logique, LA , qui est utilisée dans le corps des spécifications pour décrire le comportement de ces composantes. La troisième contribution consiste en la définition de l’opérateur de tissage qui correspond à la relation d’interconnexion entre les modules d’aspect et les modules de classe. La quatrième contribution concerne le développement d’un mécanisme de prévention qui permet de prévenir les interactions indésirables dans les systèmes orientés aspect.

Evaluation of mitochondrial function in a model of developmental programming of hypertension associated with transient neonatal oxygen exposure

UNE EXPOSITION NÉONATALE À L’OXYGÈNE MÈNE À DES MODIFICATIONS DE LA FONCTION MITOCHONDRIALE CHEZ LE RAT ADULTE Introduction: L’exposition à l’oxygène (O2) des ratons nouveau-nés a des conséquences à l’âge adulte dont une hypertension artérielle (HTA), une dysfonction vasculaire, une néphropénie et des indices de stress oxydant. En considérant que les reins sont encore en développement actif lors des premiers jours après la naissance chez les rats, jouent un rôle clé dans le développement de l’hypertension et qu’une dysfonction mitochondriale est associé à une augmentation du stress oxydant, nous postulons que les conditions délétères néonatales peuvent avoir un impact significatif au niveau rénal sur la modulation de l’expression de protéines clés du fonctionnement mitochondrial et une production mitochondriale excessive d’espèces réactives de l’ O2. Méthodes: Des ratons Sprague-Dawley sont exposés à 80% d’O2 (H) ou 21% O2 (Ctrl) du 3e au 10e jr de vie. En considérant que plusieurs organes des rats sont encore en développement actif à la naissance, ces rongeurs sont un modèle reconnu pour étudier les complications d’une hyperoxie néonatale, comme celles liées à une naissance prématurée chez l’homme. À 4 et à 16 semaines, les reins sont prélevés et les mitochondries sont extraites suivant une méthode d’extraction standard, avec un tampon contenant du sucrose 0.32 M et différentes centrifugations. L’expression des protéines mitochondriales a été mesurée par Western blot, tandis que la production d’ H202 et les activités des enzymes clés du cycle de Krebs ont été évaluées par spectrophotométrie. Les résultats sont exprimés par la moyenne ± SD. Résultats: Les rats mâles H de 16 semaines (n=6) présentent une activité de citrate synthase (considéré standard interne de l’expression protéique et de l’abondance mitochondriales) augmentée (12.4 ± 8.4 vs 4.1 ± 0.5 μmole/mL/min), une diminution de l’activité d’aconitase (enzyme sensible au redox mitochondrial) (0.11 ± 0.05 vs 0.20 ± 0.04 μmoles/min/mg mitochondrie), ainsi qu’une augmentation dans la production de H202 (7.0 ± 1.3 vs 5.4 ± 0.8 ρmoles/mg protéines mitochondriales) comparativement au groupe Ctrl (n=6 mâles et 4 femelles). Le groupe H (vs Ctrl) présente également une diminution dans l’expression de peroxiredoxin-3 (Prx3) (H 0.61±0.06 vs. Ctrl 0.78±0.02 unité relative, -23%; p<0.05), une protéine impliquée dans l’élimination d’ H202, de l’expression du cytochrome C oxidase (Complexe IV) (H 1.02±0.04 vs. Ctrl 1.20±0.02 unité relative, -15%; p<0.05), une protéine de la chaine de respiration mitochondriale, tandis que l’expression de la protéine de découplage (uncoupling protein)-2 (UCP2), impliquée dans la dispersion du gradient proton, est significativement augmentée (H 1.05±0.02 vs. Ctrl 0.90±0.03 unité relative, +17%; p<0.05). Les femelles H (n=6) (vs Ctrl, n=6) de 16 semaines démontrent une augmentation significative de l’activité de l’aconitase (0.33±0.03 vs 0.17±0.02 μmoles/min/mg mitochondrie), de l’expression de l’ATP synthase sous unité β (H 0.73±0.02 vs. Ctrl 0.59±0.02 unité relative, +25%; p<0.05) et de l’expression de MnSOD (H 0.89±0.02 vs. Ctrl 0.74±0.03 unité relative, +20%; p<0.05) (superoxide dismutase mitochondriale, important antioxidant), tandis que l’expression de Prx3 est significativement réduite (H 1.1±0.07 vs. Ctrl 0.85±0.01 unité relative, -24%; p<0.05). À 4 semaines, les mâles H (vs Ctrl) présentent une augmentation significative de l’expression de Prx3 (H 0.72±0.03 vs. Ctrl 0.56±0.04 unité relative, +31%; p<0.05) et les femelles présentent une augmentation significative de l’expression d’UCP2 (H 1.22±0.05 vs. Ctrl 1.03±0.04 unité relative, +18%; p<0.05) et de l’expression de MnSOD (H 1.36±0.01 vs. 1.19±0.06 unité relative, +14%; p<0.05). Conclusions: Une exposition néonatale à l’O2 chez le rat adulte mène à des indices de dysfonction mitochondriale dans les reins adultes, associée à une augmentation dans la production d’espèces réactives de l’oxygène, suggérant que ces modifications mitochondriales pourraient jouer un rôle dans l’hypertension artérielle et d’un stress oxydant, et par conséquent, être un facteur possible dans la progression vers des maladies cardiovasculaires. Mots-clés: Mitochondries, Reins, Hypertension, Oxygène, Stress Oxydant, Programmation

Lagrangian-informed mixed integer programming reformulations

La programmation linéaire en nombres entiers est une approche robuste qui permet de résoudre rapidement de grandes instances de problèmes d'optimisation discrète. Toutefois, les problèmes gagnent constamment en complexité et imposent parfois de fortes limites sur le temps de calcul. Il devient alors nécessaire de développer des méthodes spécialisées afin de résoudre approximativement ces problèmes, tout en calculant des bornes sur leurs valeurs optimales afin de prouver la qualité des solutions obtenues. Nous proposons d'explorer une approche de reformulation en nombres entiers guidée par la relaxation lagrangienne. Après l'identification d'une forte relaxation lagrangienne, un processus systématique permet d'obtenir une seconde formulation en nombres entiers. Cette reformulation, plus compacte que celle de Dantzig et Wolfe, comporte exactement les mêmes solutions entières que la formulation initiale, mais en améliore la borne linéaire: elle devient égale à la borne lagrangienne. L'approche de reformulation permet d'unifier et de généraliser des formulations et des méthodes de borne connues. De plus, elle offre une manière simple d'obtenir des reformulations de moins grandes tailles en contrepartie de bornes plus faibles. Ces reformulations demeurent de grandes tailles. C'est pourquoi nous décrivons aussi des méthodes spécialisées pour en résoudre les relaxations linéaires. Finalement, nous appliquons l'approche de reformulation à deux problèmes de localisation. Cela nous mène à de nouvelles formulations pour ces problèmes; certaines sont de très grandes tailles, mais nos méthodes de résolution spécialisées les rendent pratiques.