Forecasting sales trends is a valuable activity for companies of all types and sizes, as it enables more efficient decision making to avoid unnecessary expenses from excess inventory or, conversely, losses due to insufficient inventory to meet demand. In this paper, we designed a personalized cost function to reduce economic losses caused by the excessive acquisition of products or derived from their scarcity when needed. Moreover, we designed an LSTM network integrated with Glorot and Orthogonal initializers and dropout to forecast sales trends in a lumber mill in Tamaulipas, Mexico. To generalize and appropriately forecast the sales of the lumber mill products, we optimized the LSTM network’s hyperparameters through a genetic algorithm, which was essential to explore the solution space. We evaluated our proposal in instances obtained from the historical sales of the five main products sold by the lumber mill. According to the results, we concluded that for our case study the proposed function cost and the hyperparameters optimization allowed the LSTM to forecast the direction and trend of the lumber mill’s product sales despite the variability of the products.

The optimization of Artificial Neural Networks (ANNs) remains a significant challenge in machine learning, particularly in overcoming local-optima limitations during training. Traditional classification algorithms, such as k-Nearest Neighbors (KNN), decision trees, Support Vector Machines (SVMs), and ANNs, often suffer from convergence to suboptimal solutions due to their training methods. This research proposes a hybrid neuroevolutionary approach that integrates a genetic algorithm with a NEAT-based structure to enhance ANN performance. Additionally, a Cellular Processing Algorithm (PCELL) is employed to expand the search space and improve solution quality. The methodology involves designing an initial neural network trained via backpropagation, followed by the application of genetic operators to evolve network structures. Experimental results from diverse benchmark datasets demonstrate that the proposed algorithm outperforms conventional ANN training methods and achieves performance levels comparable to evolutive solutions. The results suggest that integrating evolutionary strategies with cellular processing enhances classification accuracy and contributes to the advancement of neuroevolutionary learning techniques.

Education is very important for a society’s economic, social, and cultural growth. However, educational systems still have structural problems that make it hard for students to stay in school and achieve academic success. One of the most significant problems in education is student dropout. This situation is especially impactful in high school, where its effects extend beyond the school and into long-term social and economic outcomes. This paper proposes an approach to identify data-driven indicators of dropout risk by using supervised learning and optimization methods. Our proposal consists of a supervised feature-weighted metric learning strategy that improves class separability in distance-based classifiers by reweighting features based on label information. To achieve the best possible k-nearest neighbors classification accuracy, we formulate metric learning as an optimization problem. Moreover, to optimize our proposal, we considered population-based, gradient-free metaheuristics. Furthermore, our proposed method preserves the original feature space to improve neighborhood relationships in contrast to traditional preprocessing or dimensionality reduction methods, which are important for educational outcomes. Actual school records from a high school in Ciudad Madero, Tamaulipas, Mexico, were used to conduct the experimentation to assess our proposal. Based on the experimental results, we observe an improvement in classification performance, with accuracy increasing from about 0.87 to 0.98. For statistical support, we applied a nonparametric Friedman test, which showed that these improvements are statistically significant. Hence, our proposal could be a useful and scalable method for educational data and support strategies for early identification of students at risk of dropout.