New training approaches for classification based on evolutionary neural networks. Application to product and sigmoidal units

This paper sums up the main contributions of the PhD Dissertation with an homonymous name to the current article. Specifically, three contributions to train feed-forward neural network models based on evolutionary computation for a classification task are described. The new methodologies have been evaluated in three-layered neural models, including one input, one hidden and one output layer. Particularly, two kind of neurons such as product and sigmoidal units have been considered in an independent fashion for the hidden layer. Experiments have been carried out in a good number of problems, including three complex real-world problems, and the overall assessment of the new algorithms is very outstanding. Statistical tests shed light on that significant improvements were achieved. The applicability of the proposals is wide in the sense that can be extended to any kind of hidden neuron, either to other kind of problems like regression or even optimization with special emphasis in the two first approaches

Model-Based Software Debugging

The complexity and size of software systems have rapidly increased in recent years, with software engineers facing ever-growing challenges in building and maintaining such systems. In particular, testing and debugging, that is, finding, isolating, and eliminating defects in software systems still constitute a major challenge in practiceMinisterio de Ciencia y Tecnología TIN2015-63502-C3-2-RFundacao para a Ciencia e a Tecnologia (FCT) UID/EEA/50014/2013European Regional Development Fund (ERDF) POCI-01-0145-FEDER-006961 (COMPETE 2020

The use of machine learning algorithms for the study of business profitability : a new approach based on preferences

In recent years, researchers in the Field of Artificial Intelligence have developed a learning technique, namely, preference learning, that is suitable to be used for economic analysis. The present research empirically tests one of these models, which consists of a combination of LACE and RFE algorithms. The problem of forecasting the profitability of Spanish companies upon the basis of a set of financial ratios is used as a benchmark. The model provides forecasted rankings, which are a kind of information that is more useful for the economic analysts than the forecasted class memberships that traditional machine learning techniques provide.En los últimos años, investigadores del campo de la Inteligencia Artificial han desarrollado una técnica de aprendizaje llamada “aprendizaje de referencia”, que puede usarse para el análisis económico. Esta investigación pone a prueba de manera empírica uno de estos modelos, que consiste en la combinación de algoritmos LACE y RFE. El problema de investigar los posibles beneficios de compañías españolas por encima de la base de un set de ratios financieros se usa como limitación. El modelo proporciona rankings pronosticados, los cuales son un tipo de información que es más útil para los analistas económicos que para los miembros que las técnicas que el aprendizaje tradicional proporciona

Diagnosing Business Processes

Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-

Compliance validation and diagnosis of business data constraints in business processes at runtime

Business processes involve data that can be modified and updated by various activities at any time. The data involved in a business process can be associated with flow elements or data stored. These data must satisfy the business compliance rules associated with the process, where business compliance rules are policies or statements that govern the behaviour of a company. To improve and automate the validation and diagnosis of compliance rules based on the description of data semantics (called Business Data Constraints), we propose a framework where dataflow variables and stored data are analyzed. The validation and diagnosis process is automated using Constraint Program-ming, to permit the detection and identification of possibly unsatisfiable Business Data Constraints, even if the data involved in these constraints are not all instantiated. This implies that the potential errors can be determined in advance. Furthermore, a language to describe Business Data Constraints is proposed, for the improvement of user-oriented aspects of the business process description. This language allows a business expert to write Business Data Constraints that will be automatically validated in run-time, without the support of an information technology expert.Junta de Andalucía P08-TIC-04095Ministerio de Ciencia y Tecnología TIN2009-1371

On the use of local search heuristics to improve GES-based Bayesian network learning

Bayesian networks learning is computationally expensive even in the case of sacrificing the optimality of the result. Many methods aim at obtaining quality solutions in affordable times. Most of them are based on local search algorithms, as they allow evaluating candidate networks in a very efficient way, and can be further improved by us ing local search-based metaheuristics to avoid getting stuck in local optima. This approach has been successfully applied in searching for network structures in the space of directed acyclic graphs. Other algorithms search for the networks in the space of equiva lence classes. The most important of these is GES (Greedy Equiv alence Search). It guarantees obtaining the optimal network under certain conditions. However, it can also get stuck in local optima when learning from datasets with limited size. This article proposes the use of local search-based metaheuristics as a way to improve the behaviour of GES in such circumstances. These methods also guar antee asymptotical optimality, and the experiments show that they improve upon the score of the networks obtained with GES

Thermo-economic optimisation of large solar tower power plants

Given the growing number of solar tower power plants in operation, under construction and under planning, the assessment and the optimisation of their performance are required both at the energy level and the economic level. In other words, the same way as any other conventional power plant, large solar tower power plants have to convert the incoming solar radiation into as much electric power as possible, with as less money as possible on the long run. First, the relevant local environmental conditions are identified : obviously the solar direct normal radiation and the sun’s position over time, as well as the ambient temperature and the wind velocity and direction. Then the field of heliostat mirrors is created thanks to an algorithm that allows a compromise between the density of mirrors and the interferences occurring with each other. Using the Gemasolar set-up near Sevilla in Spain as a base case, the performance of the heliostat field is simulated over three specific days and interpolated over the entire year. As a result, the annual output of the central receiver is obtained and implemented as the input for a storage system and a conventional heat-to-electricity conversion cycle. A specific operating strategy provides the overall energy performance of the plant. In parallel, the incident flux distribution on the receiver is also simulated to identify peaks and transients, especially due to clouds. Two multi-aiming strategies are investigated and allow to decrease the peaks significantly without affecting too much the total power. Subsequently, the economic performance of a solar tower plant is assessed thanks to a detailed investment cost analysis and with an estimate of financial indicators such as the levelised electricity cost and the project net present value. A thermo-economic optimisation allows then the description of optimal trade-off set-ups in comparison to the Gemasolar base case. The key decision variables are given by a sensitivity analysis that already shows a set of potential efficiency and cost improvements. The optimisation itself leads to even greater potential improvements of 24 points in field efficiency and 9 [¢/kWhel] in levelised electricity cost. Furthermore, with variable ranges limited to the parameters of Gemasolar, the best equivalent plant set-up shows a 40% smaller land area. After that, multi-tower set-ups are proposed and also implemented in the optimisation, which highlights the optimal single- to multi-tower transition size. Finally, a combination of a parabolic trough collector field and a heliostat field in the same plant is studied and turns out to make a further cost decrease possible