Luis Cárdenas Florido

Symbolic Regression (SR) aims to discover explicit mathematical expressions that describe the relationship between input variables and a target output, offering an interpretable alternative to black‐box machine learning models. Genetic Programming (GP) has been widely adopted for this purpose; however, traditional GP‐based approaches often suffer from high computational cost, limited scalability, and excessive model complexity. To address these limitations, this work presents M5GP 2.0, an extended version of the Multidimensional Multivariate Genetic Programming framework that builds upon the constructive feature induction paradigm introduced in M5GP (Parallel Multidimensional Genetic Programming with Multidimensional Populations for Symbolic Regression). M5GP 2.0 significantly expands the original framework by extending the evolutionary search space through more expressive and higher‐arity operators and introducing advanced GPU‐based optimisations for efficient large‐scale execution. The proposed method evolves symbolic feature transformations that are subsequently combined using linear models, enabling the generation of compact solutions with interpretability potential while maintaining competitive predictive performance. The experimental evaluation is conducted using a standardised and widely accepted benchmark, namely SRBench, enabling rigorous and reproducible comparisons against state‐of‐the‐art methods considering metrics such as predictive , root mean squared error (RMSE), model size, and training time. Overall, the results indicate that M5GP 2.0 constitutes a robust and scalable symbolic regression framework that achieves a favourable balance between predictive performance, model compactness, and computational efficiency, while retaining the potential for symbolic traceability and post hoc interpretability.

Wind turbine blades (WTB) are critical components of wind energy systems. Operating in harsh environments WTBs face significant challenges, since damage to their leading edge caused by erosion or additive surface roughness can reduce performance, and increase maintenance costs and operational downtime. One approach to detect WTB damage is to use machine learning, but properly designing a predictive system is not trivial. Auto machine learning (AutoML) can be used to simplify the design and implementation of machine learning pipelines. This work presents the first comparison of state‐of‐the‐art AutoML methods, Auto‐Sklearn, H2O‐DAI and TPOT, to detect erosion and additive roughness in WTBs. The Leading‐Edge Erosion Study database is used, which provides measurements of the pressure coefficient along the airfoil under different conditions. This is the first work to combine the pressure coefficient and AutoML systems to detect these types of damage. Results show the viability of using AutoML in this task, with H2O‐DAI producing the best results, achieving an accuracy above in many cases. However, statistical analysis shows that a standard classifier can achieve similar performance across all problems considered, based on the Friedman test and the Wilcoxon‐Holm post hoc analysis with an significance level. However, AutoML systems perform better as the complexity and difficulty of the problem increases.

Machine learning and artificial intelligence are growing in popularity thanks to their ability to produce models that exhibit unprecedented performance in domains that include computer vision, natural language processing and code generation. However, such models tend to be very large and complex and impossible to understand using traditional analysis or human scrutiny. Conversely, Symbolic Regression methods attempt to produce models that are relatively small and (potentially) human-readable. In this domain, Genetic Programming (GP) has proven to be a powerful search strategy that achieves state-of-the-art performance. This paper presents a new GP-based feature transformation method called M5GP, which is hybridized with multiple linear regression to produce linear models, implemented to exploit parallel processing on graphical processing units for efficient computation. M5GP is the most recent variant from a family of feature transformation methods (M2GP, M3GP and M4GP) that have proven to be powerful tools for both classification and regression tasks applied to tabular data. The proposed method was evaluated on SRBench v2.0, the current standard benchmarking suite for Symbolic Regression. Results show that M5GP achieves performance that is competitive with the state-of-the-art, achieving a top-three rank on the most difficult subset of black-box problems. Moreover, it achieves the lowest computation time when compared to other GP-based methods that have similar accuracy scores.