CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods

Exploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets

Data augmentation is rapidly gaining attention in machine learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case, the main challenge is to estimate the label associated to new synthetic patterns. This paper studies the effect of generating synthetic data by convex combination of patterns and the use of these as unsupervised information in a semi-supervised learning framework with support vector machines, avoiding thus the need to label synthetic examples. We perform experiments on a total of 53 binary classification datasets. Our results show that this type of data over-sampling supports the well-known cluster assumption in semi-supervised learning, showing outstanding results for small high-dimensional datasets and imbalanced learning problems

Hybridization of neural network models for the prediction of Extreme Significant Wave Height segments

This work proposes a hybrid methodology for the detection and prediction of Extreme Significant Wave Height (ESWH) periods in oceans. In a first step, wave height time series is approximated by a labeled sequence of segments, which is obtained using a genetic algorithm in combination with a likelihood-based segmentation (GA+LS). Then, an artificial neural network classifier with hybrid basis functions is trained with a multiobjetive evolutionary algorithm (MOEA) in order to predict the occurrence of future ESWH segments based on past values. The methodology is applied to a buoy in the Gulf of Alaska and another one in Puerto Rico. The results show that the GA+LS is able to segment and group the ESWH values, and the neural network models, obtained by the MOEA, make good predictions maintaining a balance between global accuracy and minimum sensitivity for the detection of ESWH events. Moreover, hybrid neural networks are shown to lead to better results than pure models

Building and exploiting a Digital Twin for the management of drinking water distribution networks

[EN] Digital Twins (DTs) are starting to be exploited to improve the management of water distribution systems (WDSs) and, in the future, they will be crucial for decision making. In this paper, the authors propose several requirements that a DT of a water distribution system should accomplish. Developing a DT is a challenge, and a continuous process of adjustments and learning is required. Due to the advantages of having a DT of the WDS always available, during the last years a strategy to build and maintain a DT of the water distribution network of Valencia (Spain) and its Metropolitan Area (1.6 million inhabitants) was developed. This is one of the first DTs built of a water utility, being currently in operation. The great benefits of their use in the daily operation of the system ensure that they will begin to be usual in the most advanced smart cities.Conejos Fuertes, P.; Martínez Alzamora, F.; Hervás-Carot, M.; Alonso Campos, JC. (2020). Building and exploiting a Digital Twin for the management of drinking water distribution networks. Urban Water Journal. 17(8):704-713. https://doi.org/10.1080/1573062X.2020.1771382S704713178Chacón Ramírez, E., Albarrán, J. C., & Cruz Salazar, L. A. (2019). The Control of Water Distribution Systems as a Holonic System. Studies in Computational Intelligence, 352-365. doi:10.1007/978-3-030-27477-1_27Grieves, M., et al. 2015. Virtually Intelligent Product Systems: Digital and Physical Twins. In Complex Systems Engineering: Theory and Practice, edited by S. Flumerfelt, et al., 175–200. American Institute of Aeronautics and Astronautics.Hatchett, S., J. Uber, D. Boccelli, T. Haxton, R. Janke, A. Kramer, A. Matracia, and S. Panguluri. 2011. “Real-Time Distribution System Modeling: Development, Application, and Insights.” Urban Water Management: Challenges and Oppurtunities - 11thInternational Conference on Computing and Control for the Water Industry, CCWI 2011 July.Kartakis, S., Abraham, E., & McCann, J. A. (2015). WaterBox. Proceedings of the 1st ACM International Workshop on Cyber-Physical Systems for Smart Water Networks. doi:10.1145/2738935.2738939Lin, J., Sedigh, S., & Miller, A. (2009). Towards Integrated Simulation of Cyber-Physical Systems: A Case Study on Intelligent Water Distribution. 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing. doi:10.1109/dasc.2009.140Qi, Q., & Tao, F. (2018). Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access, 6, 3585-3593. doi:10.1109/access.2018.2793265Alac, M. (2008). Working with Brain Scans. Social Studies of Science, 38(4), 483-508. doi:10.1177/0306312708089715Shi, Y., Xu, J., & Du, W. (2019). Discussion on the New Operation Management Mode of Hydraulic Engineering Based on the Digital Twin Technique. Journal of Physics: Conference Series, 1168, 022044. doi:10.1088/1742-6596/1168/2/022044Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405-2415. doi:10.1109/tii.2018.2873186Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2017). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9-12), 3563-3576. doi:10.1007/s00170-017-0233-1Tao, F., & Qi, Q. (2019). Make more digital twins. Nature, 573(7775), 490-491. doi:10.1038/d41586-019-02849-1Uber, J., S. Hatchett, S. Hooper, D. Boccelli, H. Woo, and R. Janke. 2014. Water Utility Case Study of Real-Time Network Hydaulic and Water Qualilty Modeling Using EPANET-RTX Libraries. EPA 6007R-14/350 Report. Cincinnati, Ohio: Environmental Protection Agency.Wang, Z., Song, H., Watkins, D. W., Ong, K. G., Xue, P., Yang, Q., & Shi, X. (2015). Cyber-physical systems for water sustainability: challenges and opportunities. IEEE Communications Magazine, 53(5), 216-222. doi:10.1109/mcom.2015.710566

Herramienta Autor para la Gestión de Tests Informatizados dentro del Sistema AHA!

En este artículo presentamos Test Editor, una herramienta autor para la construcción de test informatizados, tanto clásicos como adaptativos, a través del Web. Esta herramienta facilita el desarrollo y mantenimiento de diferentes tipos de test de tipo multi-opción o multi-respuesta, con el objetivo de poder utilizarlos dentro de sistemas educativos basados en web. Test editor es una herramienta modular que permite configurar varios parámetros sobre las preguntas o ítems y los tests. También proporciona información estadística sobre la utilización de los tests, que puede ser utilizada para el mantenimiento de los tests. Esta herramienta se ha integrado dentro del sistema AHA!, pero se puede utilizar también dentro de otros sistemas basados en web. Para probar el funcionamiento de la herramienta se ha utilizado en la creación de un test adaptativo para evaluar a dos grupos de alumnos de un mismo curso de extensión universitaria sobre programación en el lenguaje Java.In this paper we describe Test Editor, an authoring tool for building adaptive and classic webbased tests. This tool facilitates the development and maintenance of different types of multiple-choice tests for use in web-based education systems. Test Editor is a modular tool, which lets you configure several parameters about questions or items and tests. It also provides statistical information about tests usage that can be used in tests maintenance. We have integrated the Test Editor with the AHA! system, but it can be used in other web-based systems as well. In order to test the performance of the tool, we have used it to create an adaptive test to evaluate two groups of students of the same university extension course about Java language

Label Dependent Evolutionary Feature Weighting for Remote Sensing Data

Nearest neighbour (NN) is a very common classifier used to develop important remote sensing products like land use and land cover (LULC) maps. Evolutive computation has often been used to obtain feature weighting in order to improve the results of the NN. In this paper, a new algorithm based on evolutionary computation which has been called Label Dependent Feature Weighting (LDFW) is proposed. The LDFW method transforms the feature space assigning different weights to every feature depending on each class. This multilevel feature weighting algorithm is tested on remote sensing data from fusion of sensors (LIDAR and orthophotography). The results show an improvement on the NN and resemble the results obtained with a neural network which is the best classifier for the study area

Metrics to guide a multi-objective evolutionary algorithm for ordinal classification

Ordinal classification or ordinal regression is a classification problem in which the labels have an ordered arrangement between them. Due to this order, alternative performance evaluation metrics are need to be used in order to consider the magnitude of errors. This paper presents a study of the use of a multi-objective optimization approach in the context of ordinal classification. We contribute a study of ordinal classification performance metrics, and propose a new performance metric, the maximum mean absolute error (MMAE). MMAE considers per-class distribution of patterns and the magnitude of the errors, both issues being crucial for ordinal regression problems. In addition, we empirically show that some of the performance metrics are competitive objectives, which justify the use of multi-objective optimization strategies. In our case, a multi-objective evolutionary algorithm optimizes an artificial neural network ordinal model with different pairs of metric combinations, and we conclude that the pair of the mean absolute error (MAE) and the proposed MMAE is the most favourable. A study of the relationship between the metrics of this proposal is performed, and the graphical representation in the two-dimensional space where the search of the evolutionary algorithm takes place is analysed. The results obtained show a good classification performance, opening new lines of research in the evaluation and model selection of ordinal classifiers