Scientific modeling often requires discovering mathematical relationships that generalize across multiple related datasets while capturing context-specific deviations. We present a multiview symbolic regression method that decomposes predictions into a shared core equation and view-specific residual corrections. Through systematic ablation studies, we identify domain-informed initialization as the critical success factor: seeding the evolutionary population with common scientific building blocks (polynomials, trigonometric functions, and rational expressions) improves performance by two orders of magnitude compared to random initialization. The multiview decomposition is optimized via NSGA-II with three objectives: prediction accuracy, core complexity, and residual complexity. Additional mechanisms, including elite protection and motif-based enhancement provide consistent but marginal gains. We evaluate our approach on synthetic benchmarks with known ground-truth decompositions and on oceanographic data modeling microbial metabolism across depth layers. On the ocean dataset, our method achieves 99% lower mean squared error than standard multiview symbolic regression, gradient boosting, and neural baselines across 50 prediction targets, while producing interpretable equations that reveal temperature and oxygen as dominant environmental drivers. Our results demonstrate that effective multiview symbolic regression depends more on appropriate initialization of the search space than on sophisticated search operators.

The mining sector is a very relevant part of the Chilean economy, representing more than 14% of the country’s GDP and more than 50% of its exports. However, mining is also a high-risk activity where health, safety, and environmental aspects are fundamental concerns to take into account to render it viable in the longer term. The Chilean National Geology and Mining Service (SERNAGEOMIN, after its name in Spanish) is in charge of ensuring the safe operation of mines. On-site inspections are their main tool in order to detect issues, propose corrective measures, and track the compliance of those measures. Consequently, it is necessary to create inspection programs relying on a data-based decision-making strategy. This paper reports the work carried out in one of the most relevant dimensions of said strategy: predicting the mining worksites accident risk. That is, how likely it is a mining worksite to have accidents in the future. This risk is then used to create a priority ranking that is used to devise the inspection program. Estimating this risk at the government regulator level is particularly challenging as there is a very limited and biased data. Our main contribution is to apply a multi-task learning approach to train the risk prediction model in such a way that is able to overcome the constraints of the limited availability of data by fusing different sources. As part of this work, we also implemented a human-experience-based model that captures the procedures currently used by the current experts in charge of elaborating the inspection priority ranking. The mining worksites risk rankings built by model achieve a 121.2% NDCG performance improvement over the rankings based on the currently used experts’ model and outperforms the non-multi-task learning alternatives.

This work extends current collective intelligence evolutionary algorithms by incorporating a collective-based variation operator. As part of this work, the proposals are compared with state-of-the-art reference-point-based MOEAs: NSGA-II and RNSGA-II. Another primary objective of the work is to deal with a real-world multi-objective instance of the facility location problem. The experimental results validate the proposal. The new collective intelligence MOEA outperformed NSGA-II and R-NSGA-II for complex scenarios.

This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This representation is language-independent with no need of pretraining and produces an encoding with no information loss. It provides an adequate description of the morphology of text, as it is able to represent prefixes, declensions, and inflections with similar vectors and are able to represent even unseen words on the training dataset. Similarly, as it is compact yet sparse, is ideal for speed up training times using tensor processing libraries. As part of this paper, we show that this technique is especially effective when coupled with convolutional neural networks (CNNs) for text classification at character-level. We apply two variants of CNN coupled with it. Experimental results show that it drastically reduces the number of parameters to be optimized, resulting in competitive classification accuracy values in only a fraction of the time spent by one-hot encoding representations, thus enabling training in commodity hardware.

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse population. In this paper, we carry out a comprehensive analysis of state-of-the-art selection methods with different features aimed to determine the impact that this component has on the diversity preserved by well-known multi-objective optimisers when dealing with many-objective problems. The algorithms considered herein, which incorporate Pareto-based and indicator-based selection schemes, are analysed through their application to the Walking Fish Group (WFG) test suite taking into account an increasing number of objective functions. Algorithmic approaches are assessed via a set of performance indicators specifically proposed for measuring the diversity of a solution set, such as the Diversity Measure and the Diversity Comparison Indicator. Hypervolume, which measures convergence in addition to diversity, is also used for comparison purposes. The experimental evaluation points out that the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), where the crowding distance is replaced by the Euclidean distance, yield the best results.

It can be argued that in order to produce a sub-stantial improvement in multi-objective estimation of distribution algorithms it is necessary to focus on a particular group of issues, in particular, on the weaknesses derived from multi-objective fitness assignment and selection methods, the incorrect treatment of relevant but isolated (precursor) individuals; the loss of population diversity, and the use of ‘general purpose’ modeling algorithms without taking note of the particular requirements of the task. In this work we introduce the S-Metric Selection Estimation of Distribution Algorithm based on Multivariate Extension of Copulas (SMS-EDA-MEC). SMS-EDA-MEC was devised with the intention of dealing with those issues in mind. It builds the population model relying on the comprehensive Clayton's copula and incorporates methods for automatic population restarting and for priming precursor individuals. The experimental studies presented show that SMS-EDA-MEC yields better results than current and ‘traditional’ approaches.