Meteorological time series are frequently affected by missing observations caused by sensor failures, communication problems, maintenance activities, or data acquisition errors. Subsequent analyses, including forecasting, climate monitoring, hydrological assessment, and environmental decision-making, may therefore be biased or weakened. In this review, interpolation methods for meteorological time series are reviewed, including classical, statistical, machine learning, and deep learning approaches. Emphasis is placed on the distinction between prediction and interpolation, since both tasks are often treated as equivalent. The literature is also organized according to reconstruction strategy, including recursive, bidirectional, probabilistic or generative, and hybrid or evolutionary approaches. Current challenges, evaluation practices, open gaps, and future trends for meteorological data reconstruction are discussed. Spanish-language metadata / Metadatos en españolTítulo en español:Interpolación de series temporales meteorológicas: una revisión de la literatura sobre enfoques clásicos, de aprendizaje automático y aprendizaje profundo Resumen:Las series temporales meteorológicas se ven afectadas con frecuencia por observaciones faltantes causadas por fallos de sensores, problemas de comunicación, actividades de mantenimiento o errores en la adquisición de datos. En consecuencia, los análisis posteriores, incluidos la predicción, la monitorización climática, la evaluación hidrológica y la toma de decisiones ambientales, pueden presentar sesgos o verse comprometidos. En esta revisión se examinan los métodos de interpolación aplicados a series temporales meteorológicas, incluidos los enfoques clásicos, estadísticos, de aprendizaje automático y de aprendizaje profundo. Se hace especial hincapié en la distinción entre predicción e interpolación, dado que ambas tareas se tratan con frecuencia como equivalentes. La literatura también se organiza según la estrategia de reconstrucción empleada, incluidos los enfoques recursivos, bidireccionales, probabilísticos o generativos, e híbridos o evolutivos. Asimismo, se analizan los desafíos actuales, las prácticas de evaluación, las brechas de investigación existentes y las tendencias futuras en la reconstrucción de datos meteorológicos. Palabras Claves:Series temporales meteorológicas, interpolación, datos faltantes, imputación, redes neuronales, aprendizaje profundo, predicción de datos climáticos. Smart citations:SciteAI. Dimensions.Open Alex.

In studies of intelligent agents, the pursuit–evasion problem, commonly related to the predator–prey paradigm, has been used as a reference setting for examining decision-making, coordination, and adaptation in multi-agent systems. In this paper, pursuit and evasion strategies are reviewed from their theoretical foundations to the gradual incorporation of adaptive methods based on evolutionary algorithms and machine learning. Analytical formulations drawn from control theory, game theory, and graph-based models are considered together with learning-oriented methods, including multi-agent reinforcement learning, neuroevolution, evolutionary robotics, and competitive coevolution. The literature is arranged by methodological paradigm so that the scope, limitations, and applicability of each approach can be discussed in relation to dynamic and uncertain environments. Through this organization, classical models, algorithmic developments, and recent research trends are brought into the same discussion, while relevant gaps and possible future directions in pursuit–evasion research are identified. The contribution of this work is a structured synthesis in which analytical and adaptive perspectives are brought together within a unified reference for researchers and practitioners in robotics, artificial intelligence, and multi-agent systems.

The elaboration of school timetables is a complex and laborious task when performed manually, due to the large number of requirements and constraints that must be considered for its correct construction. In this work, the problem is addressed by using a simple Genetic Algorithms (GA) and compared with an improved approach that incorporates both an initialization function and a repair function. The study uses real data from the Computer Engineering course at the Centro Universitario UAEM Valle de México. The results obtained show that the initialization function significantly reduces errors from the first generations, while the repair function further accelerates the reduction of class or teacher splices. Thus, the effectiveness of the proposed approach to solve the scheduling problem in the university center is demonstrated. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1144Dimensions.Open Alex.