Game Theory (GT) formalizes dispute scenarios between two or more players where each one makes a move following their strategy profiles. The following paper introduces the integration of GT to selection and crossover steps of Genetic Algorithms as an evolutionary model of the representation of population in a similar way to human social evolution. Two ideas are proposed to be incorporated into the GA. First, the Genetic Algorithm with Social Interaction (GASI), a family of GAs that uses GT in selection phase to increase the diversification of the solutions. Second, the (Game-Based Crossover) GBX and GBX2 crossover operators, competition-based tournament selection methods that employ social dispute to generate more diverse offspring. Performance and robustness of the new approaches were assessed by ten continuous and constrained engineering design optimization problems and compared against variants of the canonical GA, as well as well-known heuristics from the literature. Results indicate significant performance relevance in most instances compared to other algorithms and highlight the benefits of combining GT and GA.

Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Pará, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose. The ARIMA statistical model was also used in post-processing for residual adjustment and short-term smoothing of the generated forecasts. The Framework generates probabilistic forecasts, with multivariate support, considering the following variables: daily cases per day of the first symptom, cases published daily, the occurrence of deaths, deaths published daily, and percentage of daily vaccination. The generated predictions are statistically evaluated by determining the best model for 7-day moving average projections using evaluating metrics such as MSE, RMSE, MAPE, sMAPE, r², Coefficient of Variation, and residual analysis. As a result, the generated projections showed an average error of 5.4% for Cases Publication, 8.0% for Cases Symptoms, 11.12% for Deaths Publication, and 4.6% for Deaths Occurrence, with the N-HiTS and N-BEATS models obtaining better results. In general terms, the use of deep learning models to predict cases and deaths from COVID-19 has proven to be a valuable practice for analyzing the spread of the virus, which allows health managers to better understand and respond to this kind of pandemic outbreak.

The Multi-Swarm approach allows the use of multiple configurations between two or more populations of particles, where each one can present different approaches (e.g. lbest, gbest, Unified, Guaranteed-Convergence) directed towards improving the optimization process. This article presents a proposal for local/global stochastic interconnection applied to the context of the Multi-Swarm algorithm, as well as for incrementing a local search method for refining previously obtained solutions. Two proposals are introduced for this new Multi-Swarm PSO (MSO). The first one is the inclusion of "counterpart particles", which establishes a sub-topology between inter-swarm particles, accessed by migration and evaluability rules. The other involves using customized crossover operators and is based on the BLX scheme (Blend Crossover) with direction information used as a reference for establish a subspace search around the particles. Performance and robustness of the new approaches were assessed by ten constrained engineering design optimization problems (COPs), as is compared to other solutions already published in the scientific literature. Results indicate significant performance improvements for all 10 COPs when compared to concurrent-based MSOs. By making available new references from other swarms, the counterpart particles approach tends to improve the optimization process in the search space, while an intermediate layer of local search based on a modified directed BLX crossover should provide an extra search around the particle, and thus, refining previously obtained solutions.

Magnetic lineaments are usually delineated from derivative-based images through visually selected thresholds, a subjective procedure that is difficult to reproduce over large surveys. We present an automatic workflow based on semantic segmentation. The input attribute is built in two stages: the total gradient of the total magnetic anomaly, which, for dyke-like and contact-like sources, is independent of the magnetization direction, followed by its tilt derivative, which equalizes the response of weak and strong sources within a bounded range. This combination avoids reduction to the pole, which is unstable at the low magnetic latitudes of both surveys used here. A dual-output U-Net, predicting both a lineament mask and its skeleton, was trained on aeromagnetic data from the Carajás Province and applied without retraining to the Peixoto de Azevedo area, in the Alta Floresta Gold Province. The decision threshold was calibrated by maximizing the global Dice coefficient on the validation set, giving $t^{\ast }=0.335$. Applied to a synthetic model of prescribed geometry, the workflow recovered the six bodies of 20 nT or more over 84 to 100 percent of their length, including the one striking almost north–south, whose response is attenuated sevenfold at this latitude; the bodies of 12 and 7 nT were not recovered even in noise-free data, placing the detection limit between 12 and 20 nT. The median positional error is below one grid cell, and the offset normal to the trace stays under 15 m and changes sign among the bodies, so the enhancement introduces no systematic displacement. At the regional scale, the workflow produced 1886 segments dominated by east–west orientations, consistent with the regional structural framework and unchanged when the segments are weighted by their length. The workflow offers a reproducible alternative to manual lineament mapping and delivers GIS-ready structural products for regional mapping and mineral exploration.