Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model.

This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.

Evolutionary learning of hierarchical decision rules.

This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must therefore be tried until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside of one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We tested our system on real data from the UCI repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice.

Diferencias sociodemográficas y de utilización del servicio entre primeras consultas programadas y urgentes en un Centro de Salud Mental / Sociodemographic and Service utilization differences between regular and urgent initial conultations in a co mmunity Mental Health Center

Se analiza una muestra de 718 pacientes atendidos en primera consulta, en el período de un año, en un Centro de Salud Mental (CSM). Los objetivos son: 1) valorar si existe un perfil diferencial entre los que consultaron de forma programada y los que lo hicieron por vía urgente, 2) analizar los factores relacionados con el modo de derivación. Se estudian las variables socíodemográficas y las circunstancias de la derivación manteniendo como variable dependiente la forma de derivación programada o urgente. Los resultados muestran que la consulta urgente se asocia a un varón joven, sin pareja, que acude por primera vez al centro acompañado de sus familiares quienes tomaron la iniciativa de consultar. Otros resultados son que los casos detectados por el servicio de urgencias hospitalano son derivadas al CSM por vía urgente/preferente, que los pacientes asistidos anteriormente en el CSM utilizan menos la vía urgente que los sin contacto previo o que el uso del centro según el día de la semana no es significativo (AU)