Heurísticas bioinspiradas para el problema de Floorplanning 3D térmico de dispositivos MPSoCs

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 20-06-2013Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Explicit Building Block Multiobjective Evolutionary Computation: Methods and Applications

This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of effectiveness and efficiency. The main result of this research is the development of a more robust algorithm where MOEA concepts are implicitly employed. Testing shows that the new MOEA can be more effective and efficient than previous state-of-the-art explicit BB MOEAs for selected test suite multiobjective optimization problems (MOPs) and U.S. Air Force applications. Other contributions include the extension of explicit BB definitions to clarify the meanings for good single and multiobjective BBs. A new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in finding Pareto front vectors while tracking the size of the BBs. The visualization technique is the result of a BB tracing mechanism integrated into the new MOEA that enables one to determine the required BB sizes and assign an approximation epistasis level for solving a particular problem. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classifier type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to test suite, deception, bioinformatics, unmanned vehicle flight pattern, and digital symbol set design MOPs

On the control of propagating acoustic waves in sonic crystals: analytical, numerical and optimization techniques

El control de las propiedades acústicas de los cristales de sonido (CS) necesita del estudio de la distribución de dispersores en la propia estructura y de las propiedades acústicas intrínsecas de dichos dispersores. En este trabajo se presenta un estudio exhaustivo de diferentes distribuciones, así como el estudio de la mejora de las propiedades acústicas de CS constituidos por dispersores con propiedades absorbentes y/o resonantes. Estos dos procedimientos, tanto independientemente como conjuntamente, introducen posibilidades reales para el control de la propagación de ondas acústicas a través de los CS. Desde el punto de vista teórico, la propagación de ondas a través de estructuras periódicas y quasiperiódicas se ha analizado mediante los métodos de la dispersión múltiple, de la expansión en ondas planas y de los elementos finitos. En este trabajo se presenta una novedosa extensión del método de la expansión en ondas planas que permite obtener las relaciones complejas de dispersión para los CS. Esta técnica complementa la información obtenida por los métodos clásicos y permite conocer el comportamiento evanescente de los modos en el interior de las bandas de propagación prohibida del CS, así como de los modos localizados alrededor de posibles defectos puntuales en CS. La necesidad de medidas precisas de las propiedades acústicas de los CS ha provocado el desarrollo de un novedoso sistema tridimensional que sincroniza el movimiento del receptor y la adquisición de señales temporales. Los resultados experimentales obtenidos en este trabajo muestran una gran similitud con los resultados teóricos. La actuación conjunta de distribuciones de dispersores optimizadas y de las propiedades intrínsecas de éstos, se aplica para la generación de dispositivos que presentan un rango amplio de frecuencias atenuadas. Se presenta una alternativa a las barreras acústicas tradicionales basada en CS donde se puede controlar el paso de ondas a su través. Los resultados ayudan a entender correctamente el funcionamiento de los CS para la localización de sonido, y para el guiado y filtrado de ondas acústicas.Romero García, V. (2010). On the control of propagating acoustic waves in sonic crystals: analytical, numerical and optimization techniques [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8982Palanci

Comparison of robust optimization and info-gap methods for water resource management under deep uncertainty

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.This paper evaluates two established decision-making methods and analyzes their performance and suitability within a water resources management (WRM) problem. The methods under assessment are info-gap (IG) decision theory and robust optimization (RO). The methods have been selected primarily to investigate a contrasting local versus global method of assessing water system robustness to deep uncertainty, but also to compare a robustness model approach (IG) with a robustness algorithm approach (RO), whereby the former selects and analyzes a set of prespecified strategies and the latter uses optimization algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modeling and assesses the applicability of utilizing the future flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study resembling the Sussex North Water Resource Zone in England, assessing their applicability at improving a risk-based WRM problem and highlighting the strengths and weaknesses of each method at selecting suitable adaptation strategies under climate change and future demand uncertainties. Pareto sets of robustness to cost are produced for both methods and highlight RO as producing the lower cost strategies for the full range of varying target robustness levels. IG produced the more expensive Pareto strategies due to its more selective and stringent robustness analysis, resulting from the more complex scenario ordering process.This work was financially supported by the UK Engineering and Physical Sciences Research Council, HR Wallingford and The University of Exeter through the STREAM Industrial Doctorate Centre. The authors are grateful to Dr Steven Wade, now at the Met Office, and Chris Counsell of HR Wallingford for providing data for the Sussex North case study

Structural and functional characterization of ModA and TupA proteins belonging to the molybdate and tungstate transport system from desulfovibrio alaskensis G20

Fundação para a Ciência e Tecnologia - EXPL/BBB-BEP/0274/201

Structural studies on molybdenum-dependent enzymes: from transporters to enzymes

Molybdenum (Mo) and tungsten (W) are heavy metals that can be found in the active site of several enzymes important for the metabolism of carbon, sulfur and nitrogen compounds. This Thesis describes the structural studies of two proteins that are involved in Mo and W uptake (TupA and ModA), of a Mo-containing aldehyde oxidoreductase (PaoABC) and of its chaperone PaoD. The main techniques used for the structural characterization of these proteins are X-ray crystallography and Small-Angle X-ray Scattering (SAXS), which are presented in Chapter 1, including a brief introduction about the importance of Mo and W in biological systems. Mo or W cofactor biosynthesis requires the presence of molybdate and tungstate inside the cells, which is achieved by specific ABC transport systems. Chapter 2 presents a small introduction about these transport systems, followed by the structural characterization and analysis of ModA and TupA from Desulfovibrio alaskensis G20. The tridimensional structures were determined by X-ray crystallography and SAXS, and the implication in the molybdate/tungstate uptake and discrimination between ligands discussed. The results show that TupA has a high selectivity for tungstate, while ModA is not able to distinguish between the two oxyanions. An important residue for TupA selectivity was identified, R118, paving the way for future biotechnological applications. Chapter 3 focuses on Mo-containing enzymes and cofactor maturation. The tridimensional structure of the Escherichia coli periplasmic aldehyde oxidoreductase PaoABC was solved at 1.7 Å resolution, revealing the presence of an unexpected [4Fe-4S] cluster that was not previously reported. The PaoABC structure has unique features, being the first example of an heterotrimer (αβγ) from the xanthine oxidase family. The activation of PaoABC is dependent on its interaction with the chaperone PaoD, which was also studied. The stabilization of E. coli PaoD is extremely challenging but the results here presented show that the presence of ionic liquids during thawing avoids protein aggregation. This allowed the identification of two promising crystallization conditions using polyethylene glycol and ammonium sulfate as precipitant agents. Chapter 4 describes the use of SAXS for the characterization of a multi-component biosensor to detect chronic myeloid leukemia, demonstrating the versatility of this technique to determine the envelope of biological molecules as oligonucleotides. The main conclusions derived from the work here described, as well as future perspectives, are drawn in Chapter 5

Model based test suite minimization using metaheuristics

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Application of Genetic Algorithms to Problems in Computational Fluid Dynamics

In this thesis a methodology is presented to optimise non–linear mathematical models in numerical engineering applications. The method is based on biological evolution and uses known concepts of genetic algorithms and evolutionary compu- tation. The working principle is explained in detail, the implementation is outlined and alternative approaches are mentioned. The optimisation is then tested on a series of benchmark cases to prove its validity. It is then applied to two different types of problems in computational engineering. The first application is the mathematical modeling of turbulence. An overview of existing turbulence models is followed by a series of tests of different models applied to various types of flows. In this thesis the optimisation method is used to find improved coefficient values for the k–ε, the k–ω-SST and the Spalart–Allmaras models. In a second application optimisation is used to improve the quality of a computational mesh automatically generated by a third party software tool. This generation can be controlled by a set of parameters, which are subject to the optimisation. The results obtained in this work show an improvement when compared to non–optimised results. While computationally expensive, the genetic optimisation method can still be used in engineering applications to tune predefined settings with the aim to produce results of higher quality. The implementation is modular and allows for further extensions and modifications for future applications