94 research outputs found
Measuring the similarity of protein structures by means of the universal similarity metric
Motivation: As an increasing number of protein structures
become available, the need for algorithms that can quantify
the similarity between protein structures increases as well.
Thus, the comparison of proteins’ structures, and their clustering
accordingly to a given similarity measure, is at the core of
today’s biomedical research. In this paper, we show how an
algorithmic information theory inspired Universal Similarity
Metric (USM) can be used to calculate similarities between
protein pairs.The method, besides being theoretically supported,
is surprisingly simple to implement and computationally
efficient.
Results: Structural similarity between proteins in four different
datasets was measured using the USM.The sample employed
represented alpha, beta, alpha–beta, tim–barrel, globins and
serpine protein types. The use of the proposed metric allows
for a correct measurement of similarity and classification of the
proteins in the four datasets.
Availability: All the scripts and programs used for the preparation
of this paper are available at http://www.cs.nott.ac.uk/
~nxk/USM/protocol.html. In that web-page the reader will find
a brief description on how to use the various scripts and
programs.TIC2002-04242-C03-0
Algorithm portfolio based scheme for dynamic optimization problems
Since their first appearance in 1997 in the prestigious journal Science, algorithm portfolios have become
a popular approach to solve static problems. Nevertheless and despite that success, they have not received
much attention in Dynamic Optimization Problems (DOPs). In this work, we aim at showing these methods
as a powerful tool to solve combinatorial DOPs. To this end, we propose a new algorithm portfolio
for this type of problems that incorporates a learning scheme to select, among the metaheuristics that
compose it, the most appropriate solver or solvers for each problem, configuration and search stage. This
method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad,
Deceptive and Knapsack) and compared versus two reference algorithms for these problems (Adaptive
Hill Climbing Memetic Algorithm and Self Organized Random Immigrants Genetic Algorithm). The
results showed the importance of a good design of the learning scheme, the superiority of the algorithm
portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its
performance versus the reference algorithms.Spanish Government TIN2011-27696-C02-01
TEC2013-45585-C2-2-RAndalusian Government P11-TIC-8001European CommissionBasque Government PC2013-71AIbero-American University Association for Post Graduate Studies (AUIP
PRoA: An intelligent multi-criteria Personalized Route Assistant
Personalization of pedestrian routes becomes a necessity due to the wide variety of user profiles that may differ on preferences or requirements to choose a route. Several software applications offer routes usually based on single criterion like distance or time; however, these criteria do not often fit the pedestrian needs.
Here, we will first focus on the Personalized Routes Problem and then we will approach the specific case of designing accessible and green pedestrian routes.
The proposal is implemented as a freely available Android application (named as PRoA, by intelligent multi-criteria Personalized Route Assistant), which automatically obtains geographical data and information for the decision criteria from open datasets.
The proposal is evaluated using real cases at the city of Granada, Spain.Research supported in part by projects TIN2014-55024-P (Spanish Ministry of Economy and Competitiveness) and P11-TIC-8001 (Consejería de Economía, Innovación y Ciencia, Junta de Andalucía). Both projects include FEDER funds from the European Union.
M. Torres enjoys a Ph.D. research training staff grant associated with the project TIN2014-55024-P and co-funded by the European Social Fund
A functional programming approach to a computational biology problem
Protein Folding is an important open problem in the eld of Computational Biology Due to its com binatorial nature exact polynomial algorithms to solve it could not exist and so approximation algorithms and heuristics has to be used In this paper a new heuristic is studied based on the approach that considers that the folding process is coded into the protein One important aspect of this work is that the algorithm was implemented using functional programming resulting in advantages for the understanding of the problem The results obtained are comparable with the ones obtained for classical algorithms .Eje: Conferencia latinoamericana de programación funcionalRed de Universidades con Carreras en Informática (RedUNCI
The Role of Metaheuristics as Solutions Generators
Optimization problems are ubiquitous nowadays. Many times, their corresponding
computational models necessarily leave out of consideration several characteristics and features of
the real world, so trying to obtain the optimum solution can not be enough for a problem solving
point of view. The aim of this paper is to illustrate the role of metaheuristics as solutions’ generators
in a basic problem solving framework. Metaheuristics become relevant in two modes: firstly because
every run (in the case of population based techniques) allows to obtain a set of potentially good
solutions, and secondly, if a reference solution is available, one can set up a new optimization problem
that allows to obtain solutions with similar quality in the objectives space but maximally different
structure in the design space. Once a set of solutions is obtained, an example of an a posteriori
analysis to rank them according with decision maker’s preferences is shown. All the problem solving
framework steps, emphasizing the role of metaheuristics are illustrated with a dynamic version of
the tourist trip design problem (for the first mode), and with a perishable food distribution problem
(for the second one). These examples clearly show the benefits of the problem solving framework
proposed. The potential role of the symmetry concept is also exploredProject PID2020-112754GB-I00 from MCINAEI/10.13039/
501100011033
Forgetting as a way to avoid deception in a repeated imitation game
Adversarial decision making is aimed at determining optimal decision strategies to deal with an adaptive opponent. A clear example of such situation is the repeated imitation game presented here. Two agents compete in an adversarial model where one agent wants to learn how to imitate the actions taken by the other agent by means of the observation and memorization of the past actions. One defense against this adversary is to make decisions that are intended to confuse him. To achieve this, randomized strategies that change along time for one of the agents are proposed and their performance is analysed from both a theoretical and empirical point of view. We also study the ability of the imitator to avoid deception and adapt to a new behaviour by forgetting the oldest observations. The results confirm that wrong assumptions about the imitator’s behaviour lead to dramatic losses due to a failure in causing deception.Grupo de investigación TIC-169: Modelos de Decisión y Optimización (MODO
Optimisation problems as decision problems: The case of fuzzy optimisation problems
The importance that decision-making problems and optimisation problems have today in all aspects of life is beyond all doubt. Despite that importance, both problems tend to be thought of as following different routes, when they have, in fact, a “symbiotic” relation. Here, we consider the different decision problems that arise when different kinds of information and framework of behaviour are considered, and we explore the corresponding optimisation problems that can be derived for searching the best possible decision. We explore the case where Fuzzy Mathematical Programming problems are obtained as well as other new ones in the fuzzy context.Research supported by the project TIN2014-55024-P from the Spanish Govern as well as by the project TIC-8001 from the Andalusian Govern (both financed with FEDER funds)
Aplicación de técnicas evolutivas para el problema de plegado de proteínas
La Biología Molecular se dedica fundamentalmente al estudio de la estructura y funcionalidad de proteínas y ácidos nucleicos.
A partir del descubrimiento de la estructura en doble hélice del ADN en 1953, el área ha tenido notorios avances.
El volumen de información generado a partir de la manipulación de secuencias biomoleculares y la creciente potencia de las computadoras para realizar simulaciones de procesos biológicos complejos, han provocado que los Biológos Moleculares deban interactuar con sus pares de las Ciencias de la Computación y las Matemáticas para poder aprovechar la información generada.
Como consecuencia de esta interacción surge la Biología Computacional: área que involucra el desarrollo y uso de técnicas matemáticas y de computación para facilitar el tratamiento de los problemas derivados de la Biología Molecular. Como ejemplo de este trabajo interdisciplinario podemos citar la aplicación de técnicas de bases de datos para almacenar la creciente cantidad de secuencias moleculares descubiertas, y que a través de Internet pueden consultarse, compararse para buscar similitudes y/o diferencias, etc.
Por otro lado, en los últimos 30 años ha existido un creciente interés en el desarrollo de técnicas computacionales para la resolución de problemas complejos, que se basan en la utilización de los principios de evolución y herencia.Tesis digitalizada en SEDICI gracias a la colaboración de la Biblioteca de la Facultad de Informática.Facultad de Ciencias Exacta
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