El error generador de lógica

Se propone ir más allá de lo concreto en Educación, de los desarrollos teóricos del proceso. Dado que se educa para transmitir conceptos y valores ya consensuados con un marco de referencia. Se pretenden planteamientos alternativos desde la misma estructura lógica desde donde se creó la problemática, referencia externa. La educación posee una lógica del diseño (externa), en los circuitos de percepción de lo concreto de cada discente; dejando fuera el propósito del niño/niña (interno). Al buscar la referencia externa, la percepción del alumnado se cristaliza y se suprime el acceso a todo pensamiento genuino. Más allá de un diseño, que se destruye a sí mismo por su falta de funcionalidad, motivación, etc. Este diseño, no es funcional en el momento presente, buscar la inmutabilidad del paradigma educativo en el que hemos situado a nuestra sociedad en constante cambio. Este trabajo surge a partir de la propuesta de Espacio Puente Málaga, espacio vivo de descubrimiento y aprendizaje a través de las artes, la naturaleza y el juego para niños en Alhaurín de la Torre, donde el sol, la tierra y los niños eran protagonistas. Se planteó desde la certeza del efecto de la conexión con la tierra, generadora de conciencia a través de la experiencia. Observar el proceso y asumir el margen de error en todos los diseños humanos. Error como herramienta diseñado en el abstracto y previo a la identidad particular. Abrirnos al mecanismo universal de posibilidades, “ soy con mis circunstancias, emociones” llamados “realidad”, ligada a lo real, que queda abrochada, cristalizada, validando el diseño y no permitiéndonos ver la lógica abstracta y los potenciales que la modulan . E DU CAR desde la mirada del potencial dentro de cada ser humano con su autoreferencia. La realidad se desarticula de los automatismos cuando formulamos cuestionamientos que dejamos sueltos, rompemos preguntas, las desarticulamos de las respuestas automatizadas en la linealidad.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Redesigning the jMetal Multi-Objective Optimization Framework

jMetal, an open source, Java-based framework for multi-objective optimization with metaheuristics, has become a valuable tool for many researches in the area as well as for some industrial partners in the last ten years. Our experience using and maintaining it during that time, as well as the received comments and suggestions, have helped us improve the jMetal design and identify significant features to incorporate. This paper revisits the jMetal architecture, describing its refined new design, which relies on design patterns, principles from object-oriented design, and a better use of the Java language features to improve the quality of the code, without disregarding jMetal ever goals of simplicity, facility of use, flexibility, extensibility and portability. Among the newly incorporated features, jMetal supports live interaction with running algorithms and parallel execution of algorithms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Automatic configuration of NSGA-II with jMetal and irace

jMetal is a Java-based framework for multi-objective optimization with metaheuristics providing, among other features, a wide set of algorithms that are representative of the state-of-the-art. Although it has become a widely used tool in the area, it lacks support for automatic tuning of algorithm parameter settings, which can prevent obtaining accurate Pareto front approximations, especially for inexperienced users. In this paper, we present a first approach to combine jMetal and irace, a package for automatic algorithm configuration; the NSGA-II is chosen as the target algorithm to be tuned. The goal is to facilitate the combined use of both tools to jMetal users to avoid wasting time in adjusting manually the parameters of the algorithms. Our proposal involves the definition of a new algorithm template for evolutionary algorithms, which allows the flexible composition of multi-objective evolutionary algorithms from a set of configurable components, as well as the generation of configuration files for adjusting the algorithm parameters with irace. To validate our approach, NSGA-II is tuned with a benchmark problems and compared with the same algorithm using standard settings, resulting in a new variant that shows a competitive behavior.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Tratamiento fiscal del crowdfunding en España

El nuevo modelo de financiación participativa o crowdfunding surge por las crecientes necesidades del mercado, convirtiéndose en una innovación social frente a los métodos tradicionales, basada en la búsqueda de financiación por parte de los promotores de proyectos y la respuesta ciudadana como parte inversora. En el crowdfunding se distinguen distintas tipologías con sus correspondientes diferencias, tanto conceptuales como jurídicas y tributarias en las que se encuentran la tributación a impuestos de diversa modalidad según el carácter del sujeto que lleva a cabo la operación.Universidad de Sevilla. Doble Grado en Administración y Dirección de Empresas y en Derech

A Study of the Combination of Variation Operators in the NSGA-II Algorithm

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application rate along the search process allows to improve the static classical behavior. This way, we explore the combined use of three different operators (simulated binary crossover, differential evolution’s operator, and polynomial mutation) in the NSGA-II algorithm. We have considered two strategies for selecting the operators: random and adaptive. The resulting variants have been tested on a set of 19 complex problems, and our results indicate that both schemes significantly improve the performance of the original NSGA-II algorithm, achieving the random and adaptive variants the best overall results in the bi- and three-objective considered problems, respectively.UNIVERSIDAD DE MÁLAGA. CAMPUS DE EXCELENCIA INTERNACIONAL ANDALUCÍA TEC

About Designing an Observer Pattern-Based Architecture for a Multi-objective Metaheuristic Optimization Framework

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Predicting Workflow Task Execution Time in the Cloud using A Two-Stage Machine Learning Approach

Many techniques such as scheduling and resource provisioning rely on performance prediction of workflow tasks for varying input data. However, such estimates are difficult to generate in the cloud. This paper introduces a novel two-stage machine learning approach for predicting workflow task execution times for varying input data in the cloud. In order to achieve high accuracy predictions, our approach relies on parameters reflecting runtime information and two stages of predictions. Empirical results for four real world workflow applications and several commercial cloud providers demonstrate that our approach outperforms existing prediction methods. In our experiments, our approach respectively achieves a best-case and worst-case estimation error of 1.6% and 12.2%, while existing methods achieved errors beyond 20% (for some cases even over 50%) in more than 75% of the evaluated workflow tasks. In addition, we show that the models predicted by our approach for a specific cloud can be ported with low effort to new clouds with low errors by requiring only a small number of executions

Dynamic Multi-Objective Optimization With jMetal and Spark: a Case Study

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, we make use of the streaming facilities of Spark to feed an optimization problem with data from different sources. We demonstrate the use of our tool by solving a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on near real-time traffic data from New York City, which is updated several times per minute. Our experiment shows that both jMetal and Spark can be integrated providing a software platform to deal with dynamic multi-optimization problems.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Autotuning Stencil Computations with Structural Ordinal Regression Learning

Stencil computations expose a large and complex space of equivalent implementations. These computations often rely on autotuning techniques, based on iterative compilation or machine learning (ML), to achieve high performance. Iterative compilation autotuning is a challenging and time-consuming task that may be unaffordable in many scenarios. Meanwhile, traditional ML autotuning approaches exploiting classification algorithms (such as neural networks and support vector machines) face difficulties in capturing all features of large search spaces. This paper proposes a new way of automatically tuning stencil computations based on structural learning. By organizing the training data in a set of partially-sorted samples (i.e., rankings), the problem is formulated as a ranking prediction model, which translates to an ordinal regression problem. Our approach can be coupled with an iterative compilation method or used as a standalone autotuner. We demonstrate its potential by comparing it with state-of-the-art iterative compilation methods on a set of nine stencil codes and by analyzing the quality of the obtained ranking in terms of Kendall rank correlation coefficients

HYDRA: Distributed Multi-Objective Optimization for Designers

Architectural design problems can be quite involved, as there is a plethora of – usually conflicting – criteria that one has to address in order to find an optimal, performative solution. Multi-Objective Optimization (MOO) techniques can thus prove very useful, as they provide solution spaces which can traverse the different trade-offs of convoluted design options. Nevertheless, they are not widely used as (a) they are computationally expensive and (b) the resulting solution space can be proven difficult to visualize and navigate, particularly when dealing with higher dimensional spaces. This paper will present a system, which merges bespoke multi-objective optimization with a parametric CAD system, enhanced by supercomputing, into a single, coherent workflow, in order to address the above issues. The system architecture ensures optimal use of existing compute resources and enables massive performance speed-up, allowing for fast review and delivery cycles. The application aims to provide architects, designers and engineers with a better understanding of the design space, aiding the decision-making process by procuring tangible data from different objectives and finally providing fit (and sometimes unforeseen) solutions to a design problem. This is primarily achieved by a graphical interface of easy to navigate solution spaces of design options, derived from their respective Pareto fronts, in the form of a web-based interactive dashboard. Since understanding high-dimensionality data is a difficult task, multivariate analysis techniques were implemented to post-process the data before displaying it to end users. Visual Data Mining (VDM) and Machine Learning (ML) techniques were incorporated to facilitate knowledge discovery and exploration of large sets of design options at an early design stage. The system is demonstrated and assessed on an applied design case study of a master-planning project, where the benefits of the process are more evident, especially due to its complexity and size