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

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

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

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

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)

A Metaheuristic Based Approach for the Customer-Centric Perishable Food Distribution Problem

The CNRST has awarded H. El Raoui an excellence scholarship. D. Pelta acknowledges support from projects TIN2017-86647-P (Spanish Ministry of Economy, Industry, and Competitiveness. Including FEDER funds) and PID2020-112754GB-I00 (Spanish Ministry of Science and Innovation).High transportation costs and poor quality of service are common vulnerabilities in various logistics networks, especially in food distribution. Here we propose a many-objective Customercentric Perishable Food Distribution Problem that focuses on the cost, the quality of the product, and the service level improvement by considering not only time windows but also the customers’ target time and their priority. Recognizing the difficulty of solving such model, we propose a General Variable Neighborhood Search (GVNS) metaheuristic based approach that allows to efficiently solve a subproblem while allowing us to obtain a set of solutions. These solutions are evaluated over some non-optimized criteria and then ranked using an a posteriori approach that requires minimal information about decision maker preferences. The computational results show (a) GVNS achieved same quality solutions as an exact solver (CPLEX) in the subproblem; (b) GVNS can generate a wide number of candidate solutions, and (c) the use of the a posteriori approach makes easy to generate different decision maker profiles which in turn allows to obtain different rankings of the solutions.CNRSTSpanish Ministry of Economy, Industry, and Competitiveness TIN2017-86647-PEuropean Commission TIN2017-86647-PSpanish Government PID2020-112754GB-I0

A Critical Analysis of a Tourist Trip Design Problem with Time-Dependent Recommendation Factors and Waiting Times

Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: http://github.com/cporrasn/TTDP_TDRF_WT_NWT.git.Acknowledgments: C.P. has been supported by a scholarship from AUIP Association coordinated with the Universidad de Granada. B.P.-C. was supported by the Erasmus+ programme of the European Union. The authors are grateful to the editors and the anonymous reviewers for their constructive comments and suggestions.The tourist trip design problem (TTDP) is a well-known extension of the orienteering problem, where the objective is to obtain an itinerary of points of interest for a tourist that maximizes his/her level of interest. In several situations, the interest of a point depends on when the point is visited, and the tourist may delay the arrival to a point in order to get a higher interest. In this paper, we present and discuss two variants of the TTDP with time-dependent recommendation factors (TTDP-TDRF), which may or may not take into account waiting times in order to have a better recommendation value. Using a mixed-integer linear programming solver, we provide solutions to 27 real-world instances. Although reasonable at first sight, we observed that including waiting times is not justified: in both cases (allowing or not waiting times) the quality of the solutions is almost the same, and the use of waiting times led to a model with higher solving times. This fact highlights the need to properly evaluate the benefits of making the problem model more complex than is actually needed.Projects PID2020-112754GB-I0, MCIN/AEI/10.13039/501100011033FEDER/Junta de Andalucía, Consejería de Transformación Económica, Industria, Conocimiento y Universidades/ Proyecto (B-TIC-640-UGR20

A runnable functional formal memetic algorithm framework

Historically Functional Programming FP for short has been associated with a small scope of applications mainly academic The computer science community did not pay enough attention to its potential perhaps due to the lack of e ciency of functional languages Now new theoretical developments in the eld of FP are emerging and better languages e g Haskell Concurrent and Parallel Haskell have been de ned and implemented Genetic algorithms GA are search and optimization techniques which work on a nature inspired principle the Darwinian evolution The corner idea of Darwin theory is that of natural selection The concept of natural selection is captured by GA Speci cally solutions to a given problem are codi ed in the so called chromosomes The evolution of chromosomes due to the action of crossover mutation and natural selection is simulated through computer code GA have been broadly applied and recognized as a robust search and optimization technique GA combined with a local search stage were called Memetic Algorithms after In this paper a functional framework for formal memetic algorithms is intro duced It can be easily extended by subclassi cation of the class hierarchy to provide genetic algorithm specialization memetic algorithm genetic algorithm with islands of possible solutions etc and additional genetic operators behavior To run the frame work over a particular problem a proper encoding of chromosomes should be provided with an instantiation of the genetic operators We claim that functional programming languages at least the one in which our framework has been developed Haskell have reached the necessary maturity to deal with combinatorial optimization problemsEje: TeoríaRed de Universidades con Carreras en Informática (RedUNCI