An Analysis of black-box optimization problems in reinsurance: evolutionary-based approaches

Black-box optimization problems (BBOP) are de ned as those optimization problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program). This paper is focussed on BBOPs that arise in the eld of insurance, and more speci cally in reinsurance problems. In this area, the complexity of the models and assumptions considered to de ne the reinsurance rules and conditions produces hard black-box optimization problems, that must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in BBOP, so new computational paradigms must be applied to solve these problems. In this paper we show the performance of two evolutionary-based techniques (Evolutionary Programming and Particle Swarm Optimization). We provide an analysis in three BBOP in reinsurance, where the evolutionary-based approaches exhibit an excellent behaviour, nding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

Generalized Multiobjective Multitree model solution using MOEA

In a previous paper a novel Generalized Multiobjective Multitree model (GMM-model) was proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMM-model, in this paper a multi-objective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network, with two simultaneous data flows

Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications

Unconstrained optimization with MINTOOLKIT for GNU octave

The paper documents MINTOOLKIT for GNU Octave. MINTOOLKIT provides functions for minimization and numeric differentiation. The main algorithms are BFGS, LBFGS, and simulated annealing. Examples are given.

A Model-to-model analysis of the repeated Prisoners' Dilemma: genetic algorithms vs. evolutionary dynamics

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the analytical model. Our main conclusion is that analytical and computational models are good complements for research in social sciences. Indeed, while on the one hand computational models are extremely useful to extend the scope of the analysis to complex scenar

Analysis and optimization of the satellite-to-plane link of an aeronautical global system

En aquest projecte s’ha analitzat i optimitzat l’enllaç satèl·lit amb avió per a un sistema aeronàutic global. Aquest nou sistema anomenat ANTARES està dissenyat per a comunicar avions amb estacions base mitjançant un satèl·lit. Aquesta és una iniciativa on hi participen institucions oficials en l’aviació com ara l’ECAC i que és desenvolupat en una col·laboració europea d’universitats i empreses. El treball dut a terme en el projecte compren bàsicament tres aspectes. El disseny i anàlisi de la gestió de recursos. La idoneïtat d’utilitzar correcció d’errors en la capa d’enllaç i en cas que sigui necessària dissenyar una opció de codificació preliminar. Finalment, estudiar i analitzar l’efecte de la interferència co-canal en sistemes multifeix. Tots aquests temes són considerats només per al “forward link”. L’estructura que segueix el projecte és primer presentar les característiques globals del sistema, després centrar-se i analitzar els temes mencionats per a poder donar resultats i extreure conclusions.

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flowcomputation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we develop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow computation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

An algorithm for semi-infinite polynomial optimization

"Vegeu el resum a l'inici del document del fitxer adjunt."

A linear optimization technique for graph pebbling

Graph pebbling is a network model for studying whether or not a given supply of discrete pebbles can satisfy a given demand via pebbling moves. A pebbling move across an edge of a graph takes two pebbles from one endpoint and places one pebble at the other endpoint; the other pebble is lost in transit as a toll. It has been shown that deciding whether a supply can meet a demand on a graph is NP-complete. The pebbling number of a graph is the smallest t such that every supply of t pebbles can satisfy every demand of one pebble. Deciding if the pebbling number is at most k is NP 2 -complete. In this paper we develop a tool, called theWeight Function Lemma, for computing upper bounds and sometimes exact values for pebbling numbers with the assistance of linear optimization. With this tool we are able to calculate the pebbling numbers of much larger graphs than in previous algorithms, and much more quickly as well. We also obtain results for many families of graphs, in many cases by hand, with much simpler and remarkably shorter proofs than given in previously existing arguments (certificates typically of size at most the number of vertices times the maximum degree), especially for highly symmetric graphs. Here we apply theWeight Function Lemma to several specific graphs, including the Petersen, Lemke, 4th weak Bruhat, Lemke squared, and two random graphs, as well as to a number of infinite families of graphs, such as trees, cycles, graph powers of cycles, cubes, and some generalized Petersen and Coxeter graphs. This partly answers a question of Pachter, et al., by computing the pebbling exponent of cycles to within an asymptotically small range. It is conceivable that this method yields an approximation algorithm for graph pebbling.

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases.

On the stability of the optimal value and the optimal set in optimization problems

The paper develops a stability theory for the optimal value and the optimal set mapping of optimization problems posed in a Banach space. The problems considered in this paper have an arbitrary number of inequality constraints involving lower semicontinuous (not necessarily convex) functions and one closed abstract constraint set. The considered perturbations lead to problems of the same type as the nominal one (with the same space of variables and the same number of constraints), where the abstract constraint set can also be perturbed. The spaces of functions involved in the problems (objective and constraints) are equipped with the metric of the uniform convergence on the bounded sets, meanwhile in the space of closed sets we consider, coherently, the Attouch-Wets topology. The paper examines, in a unified way, the lower and upper semicontinuity of the optimal value function, and the closedness, lower and upper semicontinuity (in the sense of Berge) of the optimal set mapping. This paper can be seen as a second part of the stability theory presented in [17], where we studied the stability of the feasible set mapping (completed here with the analysis of the Lipschitz-like property).

Route optimization and customization using real geographical data in Android mobile devices

Nowadays, there are several services and applications that allow users to locate and move to different tourist areas using a mobile device. These systems can be used either by internet or downloading an application in concrete places like a visitors centre. Although such applications are able to facilitate the location and the search for points of interest, in most cases, these services and applications do not meet the needs of each user. This paper aims to provide a solution by studying the main projects, services and applications, their routing algorithms and their treatment of the real geographical data in Android mobile devices, focusing on the data acquisition and treatment to improve the routing searches in off-line environments.

Behavioural Flexibility: evolutionary past and its role in a changing world

A través de la historia de la vida, gran parte de los organismos han desarrollado estrategias para responder a un mundo en constante cambio. Hoy en día, las actividades humanas producen cambios ambientales a una velocidad sin precedentes, lo cual se traduce en grandes desafíos para la persistencia de biodiversidad. Esta investigación evalúa las respuesta de los animales a los cambios ambientales enfocándose en la flexibilidad del comportamiento como estrategia adaptativa. En una primera aproximación a una escala evolutiva, se otorgan evidencias del vínculo hasta ahora tenue entre la cognición e historias de vida, entregando un claro apoyo a la relación entre longevidad, vida reproductiva y el tamaño del cerebro en mamíferos. La longevidad es el centro de muchas hipótesis respecto a las ventajas de desarrollar un cerebro grande, como por ejemplo en la hipótesis del buffer cognitivo y las respuestas flexibles frente a nuevos ambientes. En un segundo nivel, se abordan factores extrínsecos e intrínsecos que podrían explicar las diferencias individuales en innovación, un componente clave en la flexibilidad del comportamiento. Por medio de una aproximación experimental, se evalúan potenciales escenarios que podrían conducir a consistentes diferencias individuales en uno de los principales factores subyacentes a la innovación (i.e. la motivación), y el potencial control endocrino sobre estos escenarios. Posteriormente, con el objetivo de evaluar la respuesta de los animales frente a los cambios ambientales actuales, se explora la respuesta de los animales frente a una de las actividades humanas mas disruptivas sobre los ecosistemas, la urbanización. Por medio de un analisis filogenetico comparativo a nivel global en aves se abordan los mecanismos implicados en la perdida de biodiversidad observada en ambientes urbanos. Los resultados entregan evidencias sobre la importancia de procesos de dispersión local junto con el papel clave de los rasgos de historia de vida, pero en un sentido diferente al clasicamente pensado. Finalmente por medio de una revisión bibliográfica se entregan evidencias teóricas y empíricas que respaldan el rol clave de la flexibilidad del comportamiento en confrontar los desafíos de una vida urbana. La integración de estos resultados muestra cómo el pasado evolutivo contribuye a hacer frente a los retos ambientales actuales, y pone de relieve posibles consecuencias ante un planeta más cambiante que nunca.

Behavioural Flexibility: evolutionary past and its role in a changing world

A través de la historia de la vida, gran parte de los organismos han desarrollado estrategias para responder a un mundo en constante cambio. Hoy en día, las actividades humanas producen cambios ambientales a una velocidad sin precedentes, lo cual se traduce en grandes desafíos para la persistencia de biodiversidad. Esta investigación evalúa las respuesta de los animales a los cambios ambientales enfocándose en la flexibilidad del comportamiento como estrategia adaptativa. En una primera aproximación a una escala evolutiva, se otorgan evidencias del vínculo hasta ahora tenue entre la cognición e historias de vida, entregando un claro apoyo a la relación entre longevidad, vida reproductiva y el tamaño del cerebro en mamíferos. La longevidad es el centro de muchas hipótesis respecto a las ventajas de desarrollar un cerebro grande, como por ejemplo en la hipótesis del buffer cognitivo y las respuestas flexibles frente a nuevos ambientes. En un segundo nivel, se abordan factores extrínsecos e intrínsecos que podrían explicar las diferencias individuales en innovación, un componente clave en la flexibilidad del comportamiento. Por medio de una aproximación experimental, se evalúan potenciales escenarios que podrían conducir a consistentes diferencias individuales en uno de los principales factores subyacentes a la innovación (i.e. la motivación), y el potencial control endocrino sobre estos escenarios. Posteriormente, con el objetivo de evaluar la respuesta de los animales frente a los cambios ambientales actuales, se explora la respuesta de los animales frente a una de las actividades humanas mas disruptivas sobre los ecosistemas, la urbanización. Por medio de un analisis filogenetico comparativo a nivel global en aves se abordan los mecanismos implicados en la perdida de biodiversidad observada en ambientes urbanos. Los resultados entregan evidencias sobre la importancia de procesos de dispersión local junto con el papel clave de los rasgos de historia de vida, pero en un sentido diferente al clasicamente pensado. Finalmente por medio de una revisión bibliográfica se entregan evidencias teóricas y empíricas que respaldan el rol clave de la flexibilidad del comportamiento en confrontar los desafíos de una vida urbana. La integración de estos resultados muestra cómo el pasado evolutivo contribuye a hacer frente a los retos ambientales actuales, y pone de relieve posibles consecuencias ante un planeta más cambiante que nunca.