Optimisation of time domain controllers for supply ships using genetic algorithms and genetic programming

The use of genetic methods for the optimisation of propulsion and heading controllers for marine vessels is presented in this thesis. The first part of this work is a study of the optimisation, using Genetic Algorithms, of controller designs based on a number of different time-domain control methodologies such as PID, Sliding Mode, H8, and Pole Placement. These control methodologies are used to provide the structure for propulsion and navigation controllers for a ship. Given the variety in the number of parameters to optimise and the controller structures, the Genetic Algorithm is tested in different control optimisation problems with different search spaces. This study presents how the Genetic Algorithm solves this minimisation problem by evolving controller parameters solutions that satisfactorily perform control duties while keeping actuator usage to a minimum. A variety of genetic operators are introduced and a comparison study is conducted to find the Genetic Algorithm scheme best suited to the parameter controller optimisation problem. The performance of the four control methodologies is also compared. A variation of Genetic Algorithms, the Structured Genetic Algorithm, is also used for the optimisation of the H8 controller. The H8 controller optimisation presents the difficulty that the optimisation focus is not on parameters but on transfer functions. Structured Genetic Algorithm incorporates hierarchy in the representation of solutions making it very suitable for structural optimisation. The H8 optimisation problem has been found to be very appropriate for comparing the performance of Genetic Algorithms versus Structured Genetic Algorithm. During the second part of this work, the use of Genetic Programming to optimise the controller structure is assessed. Genetic Programming is used to evolve control strategies that, given as inputs the current and desired state of the propulsion and heading dynamics, generate the commanded forces required to manoeuvre the ship. Two Genetic Programming algorithms are implemented. The only difference between them is how they generate the numerical constants needed for the solution of the problem. The first approach uses a random generation of constants while the second approach uses a combination of Genetic Programming with Genetic Algorithms. Finally, the controllers optimised using genetic methods are evaluated through computer simulations and real manoeuvrability tests in a laboratory water basin facility

Value-at-risk portfolio optimization: a not on multiobgective genetic algoritnm

n this paper we develop a general framework for market risk optimization. The model is valid for any given risk measure. Our em- pirical procedure is focused on VaR. We solve the problem using a multiobjective genetic algorithm (GA). The algorithm is very efficient and it can handle hundreds of assets in reasonable computer time. One of the advantages of this approach is that it is easily extendable. = Рассматривается дальнейшее развитие общего подхода оптимизации риска в условиях рынка. Предлагается модель, ориентированная на различные меры риска, и эмпирическая методика расчета на основе многоцелевого генетического алгоритма

La evaluación mediante plataformas de formación: análisis de su utilización en enseñanza superior

[ES] ILas Tecnologías de la Información y Comunicaciones (TICs) han provocado una revolución no sólo en el contexto económico y social sino que han afectado también de forma muy significativa al ámbito de la enseñanza. En los últimos años, se ha puesto al alcance de las instituciones educativas diversas modalidades de plataformas de teleformación (learning management systems, LMS) que son recibidas con diferentes grados de entusiasmo por parte de los docentes. Paralelamente, el proceso de reconversión y convergencia de la enseñanza superior en España siguiendo las directrices de la Declaración de Bolonia implica un mayor protagonismo del alumno en el proceso de enseñanza-aprendizaje y en consecuencia, hace necesario un incremento en la interactividad alumno-profesor. Con el fin de ofrecer un feedback adecuado y oportuno al estudiante de sus progresos o deficiencias a lo largo del proceso, racionalizando esfuerzos y recursos, es conveniente recurrir a herramientas de tipo tecnológico que hagan más eficiente el trabajo del profesorado. Este trabajo pone de manifiesto la experiencia obtenida por un grupo de profesores de la Universitat Politècnica de València en base al uso de la plataforma de teleformación de la misma, PoliformaT, englobada dentro del proyecto Sakai. En concreto, el trabajo se ha centrado en el análisis de la herramienta “Exámenes” para la realización de tareas de evaluación formativa. Tal como se pone manifiesto en las conclusiones del trabajo, aunque la herramienta tiene asociadas unas barreras de entrada importantes -ya que requiere conocimientos avanzados de la plataforma y ciertas nociones informáticas, el balance tras su utilización es positivo y tiene un claro impacto favorable tanto en la gestión de la docencia como en el aprendizaje del alumno.Alfaro Cid, E.; Bañón Gomis, AJ.; Lajara-Camilleri, N.; Trinidad Tornel, Á. (2013). La evaluación mediante plataformas de formación: análisis de su utilización en enseñanza superior. En New changes in technology and innovation : INNODOCT'13 : International Conference on Innovation, Documentation and Teaching Technologies, held on-line in Valencia, Spain, on 6-7 May, 2013. https://riunet.upv.es/handle/10251/30843. Universidad Politécnica de Valencia. 165-173. http://hdl.handle.net/10251/82121S16517

El uso de rúbricas para una evaluación justa y objetiva en asignaturas prácticas de enseñanzas técnicas y su repercusión en el aprendizaje

[ES] En el nuevo marco de Enseñanza Superior Europeo la evaluación ha pasado de ser una herramienta de control del alumno a una parte más del proceso de aprendizaje. De hecho, una buena evaluación debe estar alineada con los objetivos de la asignatura y la metodología aplicada. En este contexto, el uso de herramientas de evaluación, como la rúbrica, tiene un gran potencial. En este trabajo se valora la utilidad del uso de rúbricas en asignaturas técnicas eminentemente prácticas. Se han tenido en consideración asignaturas de tres áreas de conocimiento dispares, como son la Ingeniería Informática, la Ingeniería Industrial y la Ingeniería de la Edificación. Todas ellas tienen en común que son asignaturas eminentemente prácticas y en las que los alumnos tienen que realizar entregas frecuentes de productos. En esta comunicación se plantea la idoneidad del uso de rúbricas en este tipo de asignaturas para la evaluación de las entregas de los alumnos. El análisis se ha realizado tanto desde el punto de vista del profesor como el de los alumnos. Los profesores aportan evidencias cuantitativas de la mejora observada en las calificaciones de los alumnos y sus impresiones personales sobre la utilidad de la herramienta. Por otra parte, se han recogido evidencias en forma de encuesta de los alumnos, en las que también valoran de forma muy positiva el uso de rúbricas, sobre todo a la hora de planificar su trabajo de cara a la entrega.Alfaro Cid, E.; Andrés Ferrer, J.; Montalva Subirats, JM.; Pérez De Los Cobos Cassinello, M. (2013). El uso de rúbricas para una evaluación justa y objetiva en asignaturas prácticas de enseñanzas técnicas y su repercusión en el aprendizaje. En New changes in technology and innovation : INNODOCT'13 : International Conference on Innovation, Documentation and Teaching Technologies, held on-line in Valencia, Spain, on 6-7 May, 2013. https://riunet.upv.es/handle/10251/30843. Universidad Politécnica de Valencia. 553-561. http://hdl.handle.net/10251/82135S55356

Genetic programming and serial processing for time series classification

This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for online or conference competitions. As there are published results of these two problems this gives us the chance to compare the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large datasets.Alfaro Cid, E.; Sharman, KC.; Esparcia Alcázar, AI. (2014). Genetic programming and serial processing for time series classification. Evolutionary Computation. 22(2):265-285. doi:10.1162/EVCO_a_00110S265285222Adeodato, P. J. L., Arnaud, A. L., Vasconcelos, G. C., Cunha, R. C. L. V., Gurgel, T. B., & Monteiro, D. S. M. P. (2009). The role of temporal feature extraction and bagging of MLP neural networks for solving the WCCI 2008 Ford Classification Challenge. 2009 International Joint Conference on Neural Networks. doi:10.1109/ijcnn.2009.5178965Alfaro-Cid, E., Merelo, J. J., de Vega, F. F., Esparcia-Alcázar, A. I., & Sharman, K. (2010). Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study. Evolutionary Computation, 18(2), 305-332. doi:10.1162/evco.2010.18.2.18206Alfaro-Cid, E., Sharman, K., & Esparcia-Alcazar, A. I. (s. f.). Evolving a Learning Machine by Genetic Programming. 2006 IEEE International Conference on Evolutionary Computation. doi:10.1109/cec.2006.1688316Arenas, M. G., Collet, P., Eiben, A. E., Jelasity, M., Merelo, J. J., Paechter, B., … Schoenauer, M. (2002). A Framework for Distributed Evolutionary Algorithms. Lecture Notes in Computer Science, 665-675. doi:10.1007/3-540-45712-7_64Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T. M., Schalk, G., Wolpaw, J. R., … Birbaumer, N. (2004). The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials. IEEE Transactions on Biomedical Engineering, 51(6), 1044-1051. doi:10.1109/tbme.2004.826692Borrelli, A., De Falco, I., Della Cioppa, A., Nicodemi, M., & Trautteur, G. (2006). Performance of genetic programming to extract the trend in noisy data series. Physica A: Statistical Mechanics and its Applications, 370(1), 104-108. doi:10.1016/j.physa.2006.04.025Eads, D. R., Hill, D., Davis, S., Perkins, S. J., Ma, J., Porter, R. B., & Theiler, J. P. (2002). Genetic Algorithms and Support Vector Machines for Time Series Classification. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V. doi:10.1117/12.453526Eggermont, J., Eiben, A. E., & van Hemert, J. I. (1999). A Comparison of Genetic Programming Variants for Data Classification. Lecture Notes in Computer Science, 281-290. doi:10.1007/3-540-48412-4_24Holladay, K. L., & Robbins, K. A. (2007). Evolution of Signal Processing Algorithms using Vector Based Genetic Programming. 2007 15th International Conference on Digital Signal Processing. doi:10.1109/icdsp.2007.4288629Kaboudan, M. A. (2000). Computational Economics, 16(3), 207-236. doi:10.1023/a:1008768404046Kishore, J. K., Patnaik, L. M., Mani, V., & Agrawal, V. K. (2000). Application of genetic programming for multicategory pattern classification. IEEE Transactions on Evolutionary Computation, 4(3), 242-258. doi:10.1109/4235.873235Kishore, J. K., Patnaik, L. M., Mani, V., & Agrawal, V. K. (2001). Genetic programming based pattern classification with feature space partitioning. Information Sciences, 131(1-4), 65-86. doi:10.1016/s0020-0255(00)00081-5Langdon, W. B., McKay, R. I., & Spector, L. (2010). Genetic Programming. International Series in Operations Research & Management Science, 185-225. doi:10.1007/978-1-4419-1665-5_7Yi Liu, & Khoshgoftaar, T. (s. f.). Reducing overfitting in genetic programming models for software quality classification. Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings. doi:10.1109/hase.2004.1281730Luke, S. (2000). Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation, 4(3), 274-283. doi:10.1109/4235.873237Luke, S., & Panait, L. (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation, 14(3), 309-344. doi:10.1162/evco.2006.14.3.309Mensh, B. D., Werfel, J., & Seung, H. S. (2004). BCI Competition 2003—Data Set Ia: Combining Gamma-Band Power With Slow Cortical Potentials to Improve Single-Trial Classification of Electroencephalographic Signals. IEEE Transactions on Biomedical Engineering, 51(6), 1052-1056. doi:10.1109/tbme.2004.827081Muni, D. P., Pal, N. R., & Das, J. (2006). Genetic programming for simultaneous feature selection and classifier design. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 36(1), 106-117. doi:10.1109/tsmcb.2005.854499Oltean, M., & Dioşan, L. (2009). An autonomous GP-based system for regression and classification problems. Applied Soft Computing, 9(1), 49-60. doi:10.1016/j.asoc.2008.03.008Otero, F. E. B., Silva, M. M. S., Freitas, A. A., & Nievola, J. C. (2003). Genetic Programming for Attribute Construction in Data Mining. Genetic Programming, 384-393. doi:10.1007/3-540-36599-0_36Poli, R. (2010). Genetic programming theory. Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO ’10. doi:10.1145/1830761.1830905Tsakonas, A. (2006). A comparison of classification accuracy of four genetic programming-evolved intelligent structures. Information Sciences, 176(6), 691-724. doi:10.1016/j.ins.2005.03.012Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., McFarland, D. J., Peckham, P. H., Schalk, G., … Vaughan, T. M. (2000). 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Dendritic cell deficiencies persist seven months after SARS-CoV-2 infection

Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 infection induces an exacerbated inflammation driven by innate immunity components. Dendritic cells (DCs) play a key role in the defense against viral infections, for instance plasmacytoid DCs (pDCs), have the capacity to produce vast amounts of interferon-alpha (IFN-α). In COVID-19 there is a deficit in DC numbers and IFN-α production, which has been associated with disease severity. In this work, we described that in addition to the DC deficiency, several DC activation and homing markers were altered in acute COVID-19 patients, which were associated with multiple inflammatory markers. Remarkably, previously hospitalized and nonhospitalized patients remained with decreased numbers of CD1c+ myeloid DCs and pDCs seven months after SARS-CoV-2 infection. Moreover, the expression of DC markers such as CD86 and CD4 were only restored in previously nonhospitalized patients, while no restoration of integrin β7 and indoleamine 2,3-dyoxigenase (IDO) levels were observed. These findings contribute to a better understanding of the immunological sequelae of COVID-19

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality