A methodology for automated design and implementation of complex analog and digital CMOS integrated circuits applying a genetic algorithm and a CAD tool for multiobjective optimization.

Tesis (Doctorado en Ciencias Naturales para el Desarrollo) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, 2014.This dissertation proposes an automated methodology to design and optimize electronic integrated circuits, something that could be called simulation-driven optimization. The concept of Pareto optimality or the so called Pareto front is introduced as a useful analysis tool in order to explore the design space of such circuits. A genetic algorithm (GA) is employed to automatically detect this front in a process that efficiently finds optimal parameterizations and their corresponding values in an aggregate fitness space. Since the problem at hand is inherently a multi-objective optimization task, many different performance measures of the circuits must be able to be easily defined and computed as fitness functions. The methodology has been validated through measurements of several fabricated test cases, using MOSIS fabrication services for a standard 0.5m CMOS technology.Instituto Tecnológico de Costa Rica. Escuela de Ingeniería Electrónica

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Development of a hybrid algorithm for bi-level bi-objective optimization, and application to hydrogen supply chain deployment and design

The present master thesis is based on the recently presented doctoral thesis of Dr. Victor Hugo Cantu Medrano, addressing multiobjective optimization problems in Process Engineering with several alternative resolution methods using Evolutionary Computation. In his thesis, a new algorithm to find the optimal design of the Hydrogen Supply Chain while minimizing economic costs and environmental impact is presented. For its resolution, the algorithm divides the problem into two subproblems or levels. The first level deals with the design of the HSC structure (sizing and location of the facilities). A second level that solves the subproblem corresponding to the operation of the supply chain (production and transportation). The technique used for its resolution is a hybridization of the MOEA SMS-EMOA, for the first level, with a linear programming solver that uses a scalarization function to address the two objectives considered in the second level. In this line, this master thesis consists of developing an extension of this same algorithm with the objective of taking advantage of all the information generated in the second level to increase its efficiency. To achieve this, the second level is executed several times for each execution of the first level, using each time a different vector of weights in the scalarization function. But this new logic implies the readaptation of the whole algorithm. First, the Hydrogen Supply Chain problem is presented and the technique for solving the original algorithm is discussed. Subsequently, the necessary modifications to the MOEA are presented in order to be able to apply the new approach to the algorithm. With the new algorithm implemented, a study is carried out for the definition of the weight vectors and different scalarization functions are studied to try to increase its efficiency. Finally, the results obtained with the new algorithm and those of the original algorithm are compared to determine whether the new version is capable of solving the same problems using fewer computational resourcesCette thèse de master est basée sur la thèse de doctorat récemment soutenue par Dr Víctor Hugo Cantú Medrano, dans laquelle il expérimente plusieurs méthodes de résolution alternatives à l'aide méthodes évolutionnaires pour résoudre les problèmes d'optimisation multiobjectifs dans le domaine du génie des procédés. Dans sa thèse, le Dr Cantú présente un nouvel algorithme permettant de trouver la conception optimale de la chaîne d'approvisionnement en hydrogène tout en minimisant les coûts économiques et l'impact environnemental. Pour sa résolution, l'algorithme divise le problème en deux sous-problèmes ou niveaux. Le premier niveau traite de la conception de la structure de la chaîne logistique hydrogène (dimensionnement et emplacement des installations). Un second niveau résout le sous-problème correspondant à l'exploitation de la chaîne logistique (production et transport). La technique utilisée pour sa résolution est une hybridation du MOEA SMS-EMOA, pour le premier niveau, avec un solveur de programmation linéaire qui utilise une fonction de scalarisation pour traiter les deux objectifs considérés dans le second niveau. Dans cette lignée, ce mémoire de master consiste à développer une extension de ce même algorithme avec l'objectif de tirer profit de toute l'information générée dans le deuxième niveau pour augmenter son efficacité. Pour ce faire, le second niveau est exécuté plusieurs fois pour chaque exécution du premier niveau, en utilisant à chaque fois un vecteur de poids différent dans la fonction de scalarisation. Mais cette nouvelle logique implique la réadaptation de l'ensemble de l'algorithme. Tout d'abord, le problème de la chaîne logistique hydrogène est présenté et la technique de résolution de l'algorithme original est discutée. Ensuite, les modifications nécessaires au MEOA sont présentées afin de pouvoir appliquer la nouvelle approche à l'algorithme. Avec le nouvel algorithme implémenté, une étude est réalisée pour la définition des vecteurs de poids et différentes fonctions de scalarisation sont étudiées pour essayer d'augmenter son efficacité. Enfin, les résultats obtenus avec le nouvel algorithme et ceux de l'algorithme original sont comparés pour déterminer si la nouvelle version est capable de résoudre les mêmes problèmes en utilisant moins de ressources informatiquesEste Trabajo Final de Master parte de la tesis doctoral recientemente presentada del doctor Víctor Hugo Cantú Medrano, donde se abordan problemas de optimización multiobjetivo en Ingeniería de Procesos experimentando con varios métodos de resolución alternativos haciendo uso de la Computación Evolutiva. En su tesis, el doctor Cantú presenta un nuevo algoritmo para encontrar el diseño óptimo de la Hydrogen Supply Chain minimizando los costes económicos y el impacto ambiental. Para su resolución, el algoritmo divide el problema en dos subproblemas o niveles. Un primer nivel que aborda el diseño de la estructura de la HSC (dimensionamiento y ubicación de las instalaciones). Un segundo nivel que resuelve el subproblema correspondiente a la operación de la cadena de suministro (producción y transporte). La técnica empleada para su resolución es una hibridación del MOEA SMS-EMOA, para el primer nivel, con un solver de programación lineal que utiliza una función de escalarización para tratar los dos objetivos considerados en el segundo nivel. En esta línea, este trabajo consiste en desarrollar una extensión de este mismo algoritmo con el objetivo de aprovechar toda la información que se genera en el segundo nivel para aumentar su eficiencia. Para lograrlo se ejecuta varias veces el segundo nivel por cada ejecución del primer nivel, utilizando cada vez un vector de pesos diferente en la función de escalarización. Pero esta nueva lógica implica la readaptación de todo el algoritmo. En primer lugar, se presenta el problema de la Hydrogen Supply Chain y se discute la técnica de resolución del algoritmo original. Posteriormente se presentan las modificaciones necesarias en el MOEA para poder aplicar el nuevo enfoque al algoritmo. Ya con el nuevo algoritmo implementado se realiza un estudio para la definición de los vectores de peso y se estudian diferentes funciones de escalarización para tratar de aumentar su eficiencia. Por último, se comparan los resultados obtenidos con el nuevo algoritmo y los del original para determinar si la nueva versión es capaz de resolver los mismos problemas utilizando un menor número de recursos computacionalesAquest Treball Final de Màster té el seu origen en la tesis doctoral recentment presentada del doctor Víctor Hugo Cantú Medrano, en la qual s’aboren problemes d’optimització multiobjectiu en enginyeria de processos, experimentant amb diversos mètodes de resolució alternatius fent ús de la Computació Evolutiva. En la seva tesis, el doctor Cantú presenta un nou algorisme per a trobar el disseny òptim de la Hydrogen Supply Chain minimitzant els costos econòmics i l’impacte ambiental. Per a la seva resolució, l’algoritme divideix el problema en dos subproblemes o nivells. Un primer nivell aborda el disseny de l’estructura.de la HSC (dimensionament i ubicació de les instal·lacions). Un segon nivell resol el subproblema corresponent a l’operació de la cadena de subministrament (producció i transport). La tècnica empleada per a la seva resolució és una hibridació del MOEA SMS-EMOA, per al primer nivell amb un solver de programació lineal que utilitza una funció d’escalarització per a tractar els dos objectius considerats en el segon nivell. En aquesta línia, aquest treball consisteix a desenvolupar una extensió d’aquest mateix algorisme amb l’objectiu d’aprofitar tota la informació que es genera en el segon nivell per a augmentar la seva eficiència. Per a aconseguir-ho s’executa diverses vegades el segon nivell per cada execució del primer nivell, utilitzant cada vegada un vector de pesos diferent en la funció d’escalarització. Però aquesta nova lògica implica la readaptació de tot l’algorisme. En primer lloc, es presenta el problema de la Hydrogen Supply Chain i es discuteix la tècnica de resolució de l’algorisme original. Posteriorment es presenten les modificacions necessàries en el MOEA per a poder aplicar el nou enfocament a l’algorisme. Ja amb el nou algorisme implementat es realitza un estudi per a la definició dels vectors de pes i s’estudien diferents funcions d’escalarització per a tractar d’augmentar la seva eficiència. Ja amb el nou algorisme implementat es realitza un estudi per a la definició dels vectors de pes i s’estudien diferents funcions d’escalarització per a tractar d’augmentar la seva eficiència. Finalment, es comparen els resultats obtinguts amb el nou algorisme i els de l’original per tal de determinar si es possible obtenir els mateixos resultats fent us d’un menor número de recursos computacionalsObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energètic

Policy making using computer simulators for complex physical systems; Bayesian decision support for the development of adaptive strategies

Policy makers increasingly rely on computer models to aid policy judgements for complex systems. The climate system, for example, is extremely complicated and its reaction to changes in radiative forcing through CO2 emissions can only be explored using models. Bayesian methods for making inferences about physical systems that combine information from computer simulators and system observations have become increasingly well studied. We apply some of these methods to the policy problem where the decisions to be made are inputs to the computer model. Particular features of our methodologies include: the provision of Bayesian decision support for the policy problem when it is known that policy may be adapted in reaction to future observations of the complex system; and careful integration of the knowledge that our computer simulators will evolve and improve over time, which may affect downstream strategies and, hence, current policy. Our methods also allow research investment questions to be explored in the context of the wider policy problem. For example, the question of whether or not an improved version of a computer simulator should be built and how much it should be run can be addressed as part of the policy problem

Reservoir Flooding Optimization by Control Polynomial Approximations

In this dissertation, we provide novel parametrization procedures for water-flooding production optimization problems, using polynomial approximation techniques. The methods project the original infinite dimensional controls space into a polynomial subspace. Our contribution includes new parameterization formulations using natural polynomials, orthogonal Chebyshev polynomials and Cubic spline interpolation. We show that the proposed methods are well suited for black-box approach with stochastic global-search method as they tend to produce smooth control trajectories, while reducing the solution space size. We demonstrate their efficiency on synthetic two-dimensional problems and on a realistic 3-dimensional problem. By contributing with a new adjoint method formulation for polynomial approximation, we implemented the methods also with gradient-based algorithms. In addition to fine-scale simulation, we also performed reduced order modeling, where we demonstrated a synergistic effect when combining polynomial approximation with model order reduction, that leads to faster optimization with higher gains in terms of Net Present Value. Finally, we performed gradient-based optimization under uncertainty. We proposed a new multi-objective function with three components, one that maximizes the expected value of all realizations, and two that maximize the averages of distribution tails from both sides. The new objective provides decision makers with the flexibility to choose the amount of risk they are willing to take, while deciding on production strategy or performing reserves estimation (P10;P50;P90)

Towards personalized disease risk prediction from metagenome analysis of the microbiome

The human gut microbiome is a central topic of research in bioinformatics and computational biology. The more and more widespread availability of high-throughput, whole-genome shotgun sequencing technologies makes it possible to obtain an unprecedented amount of genomic and metagenomic data. Various microbiota have been shown to correlate with health status and even with the early onset of diseases, the most prominent examples being Irritable Bowel Syndrome (IBS) and the more serious Inflammatory Bowel Diseases (IBD), such as Crohn's Disease (CD) and Ulcerative Colitis (UC). The desire naturally arises for predicting, based on the composition of the gut microbiome, whether patients suffer from or risk developing these conditions. This is especially valuable in the context of a newly-arising industry: holistic, personalized, preventive health consulting. Such a data-centric approach to assessing future health risks opens up the possibility for combining metagenomic information with results of other 'omics (metabolomics, genetics, proteomics, etc.), in order to provide patients with a comprehensive and easier-to-understand picture of the effects of, and the associations between, various aspects of their lifestyle. Most approaches to metagenomic analysis have so far focused on taxon-level resolution, quantifying taxa at the genus, species, or strain level. However, biological functions are not in a one-to-one correspondence with taxa: for instance, a certain species may (and does usually) fulfill more than one function, while the same function may be provided by more than one species. In this Thesis, we develop a metagenomic analysis pipeline based on individual genes and families of homologous genes. In contrast with using a large database of thousands of complete prokaryotic genomes, our approach only requires sequence data for select individual genes, therefore it is less storage-demanding. Relying on state-of-the-art read alignment software, we demonstrate gains in processing speed, too. Finally, we also show that our approach performs only slightly worse than SHOGUN, a more resource-intensive state-of-the-art metagenome analysis toolkit. We then apply both pipelines to several datasets and build machine learning models for classifying stool samples as either healthy or IBD/IBS. Finally, we analyze the association between gene families in both groups using the tools of network science applied to abundance correlation networks.ope