Designing DNA Microarrays with Ant Colony Optimization

DNA microarrays are manufactured by synthesizing pr obes on a solid surface with the help of light and a sequence of lithographic masks. Uninten tional illumination can create defects on the micro array due to small dimensions and light properties, but a suitable arrangement of probes can reduce the probability of defects. The problem of designing DN A microarrays is computationally hard and there is no publicly available algorithm that can solve this pr oblem exactly, in polynomial time. This study inves tigates the suitability of the ant colony optimization (ACO ) metaheuristic for finding optimal or at least goo d microarray designs. This research is based on a MAX -MIN ant system variant that is enhanced with 2-opt local optimization and max- κ -best pheromone reinforcement strategy. Experiments were conducted on problem instances based on border length and confli ct index models. The proposed algorithm found solut ions that are better than the best solutions previously published for 10 out of 14 problem instances

The Effects of Ant Colony Optimization on Graph Anonymization

The growing need to address privacy concerns whensocial network data is released for mining purposes hasrecently led to considerable interest in varioustechniques for graph anonymization. These techniquesand definitions, although robust are sometimes difficultto achieve for large social net-works. In this paper, welook at applying ant colony opti-mization (ACO) to twoknown versions of social network anonymization,namely k-label sequence anonymity, known to be NPhardfor k ≥ 3. We also apply it to the more recent workof [23] and Label Bag Anonymization. Ants of the artificialcolony are able to generate successively shortertours by using information accumulated in the form ofpheromone trails deposited by the edge colonies ant.Computer simu-lations have indicated that ACO arecapable of generating good solutions for known hardergraph problems.The contributions of this paper are two fold: welook to apply ACO to k-label sequence anonymity andk=label bag based anonymization, and attempt to showthe power of ap-plying ACO techniques to socialnetwork privacy attempts. Furthermore, we look tobuild a new novel foundation of study, that althoughat its preliminary stages, can lead it ground breakingresults down the road

Rank-based ant system with originality reinforcement and pheromone smoothing

Ant Colony Optimization (ACO) encompasses a family of metaheuristics inspired by the foraging behaviour of ants. Since the introduction of the first ACO algorithm, called Ant System (AS), several ACO variants have been proposed in the literature. Owing to their superior performance over other alternatives, the most popular ACO algorithms are Rank-based Ant System (ASRank), Max-Min Ant System (MMAS) and Ant Colony System (ACS). While ASRank shows a fast convergence to high-quality solutions, its performance is improved by other more widely used ACO variants such as MMAS and ACS, which are currently considered the state-of-the-art ACO algorithms for static combinatorial optimization problems. With the purpose of diversifying the search process and avoiding early convergence to a local optimal, the proposed approach extends ASRank with an originality reinforcement strategy of the top-ranked solutions and a pheromone smoothing mechanism that is triggered before the algorithm reaches stagnation. The approach is tested on several symmetric and asymmetric Traveling Salesman Problem and Sequential Ordering Problem instances from TSPLIB benchmark. Our experimental results show that the proposed method achieves fast convergence to high-quality solutions and outperforms the current state-of-the-art ACO algorithms ASRank, MMAS and ACS, for most instances of the benchmark.This research work was funded by the European project PDE-GIR of the European Union’s Horizon 2020 research & innovation program (Marie Sklodowska-Curie action, grant agreement No 778035), and by the Spanish government project #PID2021-127073OB-I00 of the MCIN/AEI/10.13039/501100011033/FEDER, EU “Una manera de hacer Europa”

Mejorando las técnicas de verificación de wrappers web mediante técnicas bioinspiradas y de clasificación

Muchas Aplicaciones Empresariales necesitan de los wrappers para poder tratar con información proveniente de la web profunda. Los wrappers son sistemas automáticos que permiten navegar, extraer, estructurar y verificar información relevante proveniente de la web. Uno de sus elementos, el extractor de información, está formado por una serie de reglas de extracción que suelen estar basadas en etiquetas HTML. Por tanto, si las fuentes cambian, el wrapper, en algunos casos, puede devolver información no deseada por la empresa y provocar, en el mejor de los casos, retrasos en sus tomas de decisión. Diversos sistemas de verificación de wrappers se han desarrollado con el objetivo de detectar automáticamente cuándo un wrapper está extrayendo datos incorrectos. Estos sistemas presentan una serie de carencias cuyo origen radica en asumir que los datos a verificar siguen una serie de características estadísticas preestablecidas. En esta disertación se analizan estos sistemas, se diseña un marco de trabajo para desarrollar verificadores y se aborda el problema de la verificación desde dos puntos de vista distintos. Inicialmente lo ubicaremos dentro de la rama de la optimización computacional y lo resolveremos aplicando metaheúristicas bioinspiradas como es la basada en colonias en hormigas, en concreto aplicaremos el algoritmo BWAS; con posterioridad, lo formularemos y resolveremos como si de un problema de clasificación no supervisada se tratara. Fruto de este segundo enfoque surge MAVE, un verificador multinivel cuya base principal son los clasificadores de una única clase.Many Enterprise Applications require wrappers to deal with information from the deep web. Wrappers are automated systems that allow you to navigate, extract, reveal structures and verify information from the web. One of its elements, the information extractor, is formed by extraction rules series that are usually based on HTML tags. Therefore, if you change sources, the wrapper, in some cases, may return unwanted information by the company and cause, at the best, delays in their decision-making process. Some wrappers verification systems have been developed to automatically detect when a wrapper is taking out incorrect data. These systems have a number of shortcomings whose origin lies in assuming that the data to verify follow a series of pre statistics. This dissertation analyzes these systems, a framework is designed to develop verifiers and the verification problem is approached from two different points of view. Initially, we place it within the branch of computational optimization and solve it applying bio-inspired metaheuristic as it is found in ant colonies, specifically we will apply the BWAS algorithm. Subsequently we will formulate and solve as if it were a unsupervised classification problem. The result of this second approach is MAVE, a multilevel verifier whose main base are the unique class classifiers

Factores determinantes en la selecci?n de vivienda social en el Per? : el caso de Chincha

El d?ficit habitacional en Am?rica Latina es muy grande, tanto por la carest?a de viviendas disponibles como por las deficiencias f?sicas de las viviendas existentes. De acuerdo con diferentes estimaciones, este d?ficit es equivalente a poco m?s de la mitad de todas las viviendas existentes. El Per? no es la excepci?n. Seg?n el Instituto Nacional de Estad?stica e Inform?tica (INEI), el d?ficit habitacional para el 2015 a escala nacional era del 12.9%, y el Ministerio de Vivienda, Construcci?n y Saneamiento se?ala que el Per? es el tercer pa?s con mayor d?ficit habitacional de Am?rica Latina. Ante una oferta escasa, el d?ficit habitacional genera un incremento del precio de las viviendas. De acuerdo con la C?mara Peruana de la Construcci?n, en los ?ltimos a?os ha aumentado el precio promedio de los departamentos en Lima y Callao. Entre el 2015 y el 2016, el precio promedio del metro cuadrado creci? en 5.5%; por zonas, la mayor alza se present? en Lima moderna, donde pas? de 4,794 soles a 5,187 soles. Diversas investigaciones que buscan determinar los factores que influyen en las decisiones de compra de vivienda en otros pa?ses indican que las preferencias del consumidor est?n dadas no solo por elementos monetarios como el precio, sino tambi?n por otros motivos, como el dise?o o la ubicaci?n de la vivienda. Desde esta perspectiva, el presente estudio explora tal problem?tica con base en una muestra de conveniencia aplicada en la provincia de Chincha, departamento de Ica. Para ello se plantea preguntas sobre qu? factores son determinantes en la selecci?n y compra de una vivienda de car?cter social y cu?l es la importancia relativa de ellos en una provincia del Per?. Estas cuestiones no han sido abordadas en la literatura acad?mica hasta el momento, aunque ya se haya hecho en otros pa?ses, como Ecuador, Colombia, Espa?a y Chile

Factores determinantes en la selecci?n de vivienda social en el Per? : evidencia de un estudio en Chincha

Este estudio tuvo por objetivo determinar atributos m?s importantes que el comprador de vivienda social tiene en cuenta al momento de optar por una vivienda a fin de que las diferentes entidades privadas y no privadas las puedan tomar en cuenta al momento de realizar los dise?os de sus proyectos inmobiliarios de vivienda social. La hip?tesis planteada es que existe un grupo de factores que son determinantes en el momento de la decisi?n de compra de la vivienda social en nuestro pa?s. Adicionalmente se busca determinar el orden de importancia o preferencia entre estos factores

Estudio e implementación de metaheurísticas para solucionar el problema de la selección deseada

Evolutionary algorithms are among the most successful approaches for solving a number of problems where systematic search in huge domains must be performed. One problem of practical interest that falls into this category is known as The Root Identification Problem in Geometric Constraint Solving, where one solution to the geometric problem must be selected among a number of possible solutions bounded by an exponential number. In this work we analize habilities and drawbacks of a series of metaheuristics in relation with the Root identification problem.Postprint (published version