Optimization Approaches For Solving String Selection Problems
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Optimization Approaches for Solving String Selection Problems by Elisa Pappalardo,Panos M. Pardalos,Giovanni Stracquadanio Pdf
Optimization Approaches for Solving String Selection Problems provides an overview of optimization methods for a wide class of genomics-related problems in relation to the string selection problems. This class of problems addresses the recognition of similar characteristics or differences within biological sequences. Specifically, this book considers a large class of problems, ranging from the closest string and substring problems, to the farthest string and substring problems, to the far from most string problem. Each problem includes a detailed description, highlighting both biological and mathematical features and presents state-of-the-art approaches. This Brief provides a quick introduction of optimization methods for string selection problems for young scientists and a detailed description of the mathematical and computational methods developed for experts in the field of optimization who want to deepen their understanding of the string selection problems. Researchers, practitioners and graduate students in the field of Computer Science, Operation Research, Mathematics, Computational Biology and Biomedicine will find this book useful.
Metaheuristics for String Problems in Bio-informatics by Christian Blum,Paola Festa Pdf
So-called string problems are abundant in bioinformatics and computational biology. New optimization problems dealing with DNA or protein sequences are constantly arising and researchers are highly in need of efficient optimization techniques for solving them. One obstacle for optimization practitioners is the atypical nature of these problems which require an interdisciplinary approach in order to solve them efficiently and accurately.
Proc. of the Fourth Brazilian Symp. on Mathematical and Computational Biology vol.2: First International Symposium on Mathematical and Computational Biology by Anonim Pdf
Computational Collective Intelligence by Ngoc Thanh Nguyen,Elias Pimenidis,Zaheer Khan,Bogdan Trawiński Pdf
This two-volume set (LNAI 11055 and LNAI 11056) constitutes the refereed proceedings of the 10th International Conference on Collective Intelligence, ICCCI 2018, held in Bristol, UK, in September 2018 The 98 full papers presented were carefully reviewed and selected from 240 submissions. The conference focuses on knowledge engineering and semantic web, social network analysis, recommendation methods and recommender systems, agents and multi-agent systems, text processing and information retrieval, data mining methods and applications, decision support and control systems, sensor networks and internet of things, as well as computer vision techniques.
Evolutionary Computation in Combinatorial Optimization by Christian Blum,Gabriela Ochoa Pdf
This book constitutes the refereed proceedings of the 14th European Conference on Evolutionary Computation in Combinatorial Optimization, Evo COP 2014, held in Granada, Spain, in April 2014, co-located with the Evo*2014 events Euro GP, Evo BIO, Evo MUSART and Evo Applications. The 20 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers cover the following topics: swarm intelligence algorithms, fitness landscapes and adaptive algorithms, real world and routing problems and cooperative and metaheuristic search.
Solving competitive location problems via memetic algorithms. High performance computing approaches. by Juana López Redondo Pdf
La localización de servicios (“Facility location” en inglés) pretende encontrar el emplazamiento de uno o más centros (servicios) de modo que se optimice una determinada función objetivo. Dicha función objetivo puede, por ejemplo, tratar de minimizar el coste de transporte, proporcionar a los clientes un servicio de forma equitativa, capturar la mayor cuota de mercado posible, etc. La localización de servicios abarca muchos campos, como la investigación operativa, la ingeniería industrial, la geografía, la economía, las matemáticas, el marketing, el planning urbanístico, además de otros muchos campos relacionados. Existen muchos problemas de localización en la vida real, como por ejemplo, la localización de hospitales, de colegios o vertederos, por nombrar algunos. Para ser capaces de obtener soluciones a los problemas de localización, es necesario desarrollar/diseñar un modelo que represente la realidad lo más fielmente posible. Dichos modelos pueden llegar a ser realmente difíciles de tratar. Muchos algoritmos de optimización global, exactos y heurísticos han sido propuestos para resolver problemas de localización. Los algoritmos exactos se caracterizan por ser capaces de obtener el óptimo global con una cierta precisión. Sin embargo, suelen ser altamente costosos desde el punto de vista computacional, lo que implica que, en determinados casos, sea imposible aplicarlos para resolver un problema. Los algoritmos heurísticos se alzan entonces como una buena alternativa. No obstante, en determinadas circunstancias, los requerimientos computacionales son tan elevados, que el uso de algoritmos heurísticos ejecutándose en procesadores estándares no es suficiente. En tales situaciones, la computación de altas prestaciones es necesaria. Esta tesis, “Solving competitive location problems via memetic algorithms. High performance computing approaches” (Algoritmos meméticos para problemas de localización competitiva. Computación de altas prestaciones), proporciona, por un lado, algoritmos heurísticos capaces de resolver problemas de localización, tanto en el dominio continuo como en el discreto y, por otro lado, técnicas paralelas que permiten reducir el tiempo de ejecución, resolver problemas más grandes, e incluso en ocasiones mejorar la calidad de las soluciones. Esta tesis incluye tres partes bien diferenciadas, cada una de las cuales se divide en varios capítulos. La primera parte Preliminaries (Preliminares), está compuesta por tres capítulos que revisan el estado actual de la optimización global, de la computación de altas prestaciones y de la ciencia de la localización, respectivamente. El Capítulo 1 comienza con la definición de los problemas de optimización, y continúa con la introducción de diferentes métodos heurísticos para tratar con ellos. El Capítulo 2 describe brevemente algunas de las arquitecturas paralelas y de los modelos de programación paralelos. Finalmente, en el Capítulo 3, se describen y analizan los principales ingredientes de la localización de servicios, y se presenta una revisión sobre problemas de localización continuos y discretos. La segunda parte de la tesis, Solving continuous location problems (Resolviendo problemas de localización continua), comienza en el Capítulo 4, donde se presenta un problema de localización competitiva en el plano y se revisan dos técnicas previamente propuestas en la literatura para resolverlo. Posteriormente, se describe una nuevo algoritmo evolutivo para resolver óptimamente el problema, llamado UEGO, y se comparan todas las alternativas. Finalmente, varias estrategias paralelas basadas en el algoritmo UEGO son analizadas y evaluadas. En el Capítulo 5, el problema de localizar un solo centro en el plano, se extiende al caso en el que la cadena o empresa quiere emplazar más de un servicio. Para abordar este problema, se adapta el algoritmo UEGO presentado en el Capítulo 4, además de otras técnicas descritas en la literatura. A través de un extenso estudio computational, todas los algoritmos son comparados y se concluye que UEGO es el mejor de todos ellos, tanto por su eficiencia como por su efectividad. UEGO es usado para realizar un estudio de sensibilidad con el fin de chequear los cambios de diseño/localización óptima cuando los parámetros del modelo cambian. Finalmente, se presentan y evalúan varias técnicas paralelas para tratar el problema de localización de varios centros. El Capítulo 6 está dedicado al problema de líder-seguidor. En dicho problema, tras la localización del líder, el competidor reacciona localizando otro nuevo centro en el lugar que maximice su propio beneficio. El objetivo del líder es encontrar la solución que maximice su beneficio, sabiendo que posteriormente, la competencia localizará un nuevo centro. Por tanto, hay que resolver dos problemas simultáneamente: el problema del seguidor, también denominado medianoide, y el problema del líder o centroide. El modelo del problema del líder-seguidor se describe al principio del capítulo. Posteriormente, se proponen y evalúan varios algoritmos para resolver tanto el problema del medianoide como el del centroide. El capítulo finaliza con la paralelización de uno de los algoritmos propuestos. La tercera parte de la tesis, Solving discrete location problems (Resolviendo problemas de localización discreta), comienza en el Capítulo 7 con una introducción sobre algunos problemas de localización discreta. Este capítulo analiza aquellos casos en los que dichos problemas podrían presentar varias soluciones óptimas. Además, se muestra cómo un usuario experimentado podría obtenerlas, y se establecen algunos criterios para seleccionar una solución óptima entre diferentes alternativas. El capítulo finaliza con la descripción del algoritmo MSH, un heurístico ampliamente usado en la literatura para la resolución de problemas de localización discreta. El Capítulo 8 describe un algoritmo genético multimodal, GASUB, capaz de resolver varios problemas de localización discreta. El algoritmo tiene diferentes parámetros de entrada que pueden ser ajustados para alcanzar diferentes metas. En este capítulo, el objetivo es obtener al menos una solución óptima, pero invirtiendo el menor esfuerzo (tiempo) computacional posible. Para tal fin, se lleva a cabo un estudio previo y se determina el conjunto de parámetros adecuado. GASUB, con este conjunto de parámetros, es comparado con el optimizador Xpress-MP y con la heurística MSH, los cuales son capaces de obtener un único óptimo global (de manera directa). Sin embargo, teniendo en cuenta que los problemas de localización discreta considerados en esta tesis pueden tener más de una solución óptima, en el Capítulo 9 se analiza la posibilidad de explotar las propiedades multimodales de GASUB. Con este fin, se propone un nuevo conjunto de parámetros, con el que GASUB es nuevamente evaluado. Finalmente, se da una paralelización de GASUB y se estudian algunas de las soluciones globales encontradas por los algoritmos. La tesis finaliza con un resumen sobre los principales resultados obtenidos y sobre la líneas de investigación futura.
Computational Approaches to Materials Design: Theoretical and Practical Aspects by Datta, Shubhabrata,Davim, J. Paulo Pdf
The development of new and superior materials is beneficial within industrial settings, as well as a topic of academic interest. By using computational modeling techniques, the probable application and performance of these materials can be easily evaluated. Computational Approaches to Materials Design: Theoretical and Practical Aspects brings together empirical research, theoretical concepts, and the various approaches in the design and discovery of new materials. Highlighting optimization tools and soft computing methods, this publication is a comprehensive collection for researchers, both in academia and in industrial settings, and practitioners who are interested in the application of computational techniques in the field of materials engineering.
Mine Planning and Equipment Selection 1997 by R. Farana,V. Kebo,L. Smutny,V. Strakos Pdf
Presenting current and emerging technologies in the field of mine planning and equipment, this volume also covers control and automation for surface and underground mining. A wide range of papers from professionals in Europe, South America, Africa and Australia are featured.
Practical Applications of Computational Intelligence Techniques by Lakhmi Jain,Philippe De Wilde Pdf
Computational intelligence paradigms have attracted the growing interest of researchers, scientists, engineers and application engineers in a number of everyday applications. These applications are not limited to any particular field and include engineering, business, banking and consumer electronics. Computational intelligence paradigms include artificial intelligence, artificial neural networks, fuzzy systems and evolutionary computing. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimisation applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge. Practical Applications of Computational Intelligence Techniques contains twelve chapters providing actual application of these techniques in the real world. Such examples include, but are not limited to, intelligent household appliances, aerial spray models, industrial applications and medical diagnostics and practice. This book will be useful to researchers, practicing engineers/scientists and students, who are interested in developing practical applications in a computational intelligence environment.
Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery.
Selecting Models from Data by P. Cheeseman,R.W. Oldford Pdf
This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.
Metaheuristics for String Problems in Bio-informatics by Christian Blum,Paola Festa Pdf
So-called string problems are abundant in bioinformatics and computational biology. New optimization problems dealing with DNA or protein sequences are constantly arising and researchers are highly in need of efficient optimization techniques for solving them. One obstacle for optimization practitioners is the atypical nature of these problems which require an interdisciplinary approach in order to solve them efficiently and accurately.
Advances in Smart Grid Technology by Ning Zhou,S. Hemamalini Pdf
This book comprises the select proceedings of the International Conference on Power Engineering Computing and Control (PECCON) 2019. This volume covers several important topics such as optimal data selection and error-free data acquiring via artificial intelligence and machine learning techniques, information and communication technologies for monitoring and control of smart grid components, and data security in smart grid network. In addition, it also focuses on economics of renewable electricity generation, policies for distributed generation, smart eco-structures and systems. This book can be useful for beginners, researchers as well as professionals interested in the area of smart grid technology.
Parallel Problem Solving from Nature, PPSN XI by Robert Schaefer,Carlos Cotta,Joanna Kolodziej,Günter Rudolph Pdf
We are very pleased to present to you this LNCS volume, the proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). PPSN is one of the most respected and highly regarded c- ference series in evolutionary computation, and indeed in natural computation aswell.Thisbiennialeventwas?rstheldinDortmundin1990, andtheninBr- sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006) and again in Dortmund in 2008. PPSN 2010 received 232 submissions. After an extensive peer review p- cess involving more than 180 reviewers, the program committee chairs went through all the review reports and ranked the papers according to the revi- ers’comments. Each paper wasevaluated by at least three reviewers.Additional reviewers from the appropriate branches of science were invoked to review into disciplinary papers. The top 128 papers were ?nally selected for inclusion in the proceedings and presentation at the conference. This represents an acceptance rate of 55%, which guarantees that PPSN will continue to be one of the c- ferences of choice for bio-inspired computing and metaheuristics researchers all over the world who value the quality over the size of a conference. The papers included in the proceedingsvolumes covera wide range of topics, fromevolutionarycomputationto swarmintelligence, frombio-inspiredcomp- ing to real-world applications. Machine learning and mathematical games s- portedbyevolutionaryalgorithmsaswellasmemetic, agent-orientedsystemsare also represented. They all are the latest and best in natural computation. The proceedings are composed of two volumes divided into nine thematic sections.
Statistical Data Mining and Knowledge Discovery by Hamparsum Bozdogan Pdf
Massive data sets pose a great challenge to many cross-disciplinary fields, including statistics. The high dimensionality and different data types and structures have now outstripped the capabilities of traditional statistical, graphical, and data visualization tools. Extracting useful information from such large data sets calls for novel approache
