Allan de Lima

Grammatical Evolution (GE) has demonstrated broad success across various domains, from automated circuit synthesis to explainable machine learning. Traditional GE systems, which rely on Context-Free Grammars (CFGs) for genotype-to-phenotype mapping, ensure syntactic correctness but inherently lack mechanisms to enforce semantic constraints. While Attribute Grammars (AGs) were introduced to GE to address this by embedding semantic checks into grammar productions, their rigid structure and computational overhead restrict adaptability to dynamic, context-sensitive requirements. This paper introduces Dynamically Attributed Grammatical Evolution (DAGE), a framework that augments CFGs by allowing the use of functions as non-terminals within the grammar. This enables direct function invocation during genotype-to-phenotype mapping, making semantic checks more straightforward and facilitating runtime-aware behaviour that traditional AGs struggle to capture. By embedding functions directly into the grammar, DAGE can perform on-the-fly constraint checking, adapt to runtime conditions, and interact with external systems as needed. This leads to more expressive representations tailored to complex requirements. Furthermore, offloading complexity into reusable functions simplifies grammar design and maintenance. DAGE is implemented in Python, leveraging the GRAPE and DEAP frameworks. Empirical evaluations on symbolic regression, multiple knapsack, artificial ant, and program synthesis benchmarks show that DAGE adapts well to domains requiring semantic or context-sensitive checking. The results show consistent benefits on constraint-heavy construction tasks. As expected, this comes with a trade-off between improved semantic control and additional mapping overhead.

Fuzzy Pattern Trees (FPTs) are symbolic tree-based structures whose internal nodes are fuzzy operators, and the leaves are fuzzy features, which enhance interpretability by representing data with meaningful fuzzy terms. However, conventional FPT approaches typically employ uniformly distributed membership functions, which often fail to accurately represent features in real-world datasets. In this work, we propose an automatic method to adapt the bounds of fuzzy features based on their data distributions, with a focus on a simple triangular membership scheme. We evaluate our approach across 11 benchmark classification problems, incorporating six parsimony pressure methods to promote more compact solutions. Our results demonstrate that the adapted fuzzification scheme, beyond improving interpretability, consistently yields models that better balance accuracy and size when compared to uniform representations, appearing on the Pareto front 20 times, while the second-best scheme appeared only 15 times.

Machine learning has significantly advanced breast cancer diagnosis, yet challenges such as high-dimensional data, severe class imbalance, and limited interpretability persist. To address these issues, we proposed a Grammatical Evolution (GE)-based Feature Selection (FS) approach, integrated with a class-balancing technique called STEM, which combines Synthetic Minority Oversampling Technique, Edited Nearest Neighbour and Mixup, effectively handling both inter-class and intra-class imbalance. Our study evaluates the performance of the GE-based FS method against other FS models, including Logistic Regression (LR) and Extreme Gradient Boosting (XGBoost), in identifying critical features for breast cancer diagnosis. The results demonstrate that the GE-based FS method effectively identifies critical features and achieves superior Area Under the Curve (AUC) scores, particularly with smaller subsets of features, unlike LR and XGBoost, which perform optimally with the full feature set. The analysis was conducted on the Digital Database for Screening Mammography and Wisconsin Breast Cancer datasets, which originally contained 52 and 30 features, respectively. The GE-based FS produces the highest AUC with subsets of 10 and 15 features, while LR and XGBoost achieve their best results using the entire feature set, underscoring the superiority of the GE-based FS method.

This paper introduces a novel mechanism, Feature Encapsulation by Stages (FES), to encapsulate and transfer features as knowledge in a staged manner within the evolutionary process. Encapsulation happens via input space expansion in one or more stages by adding the best-of-run individual as an additional input. This input space expansion is managed by augmenting the grammar. We study the feasibility of dynamically modifying the grammar and reinitialising the population to make way for new individuals which quickly evolve to a better fitness level. Five different approaches to stage management are examined. In addition, three different selection processes, namely, Tournament, Lexicase and Lexi², are used to investigate which is best suited to use with our encapsulation procedure. We benchmark our procedure on two problem domains, Boolean and Classification, and demonstrate these staging strategies lead to significantly better results. Statistical tests show our FES outperforms the standard baseline in all Boolean problems, with a 4-stage version performing best, obtaining significant differences in all Boolean problems.

Grammatical Evolution is a Genetic Programming variant which evolves problems in any arbitrary language that is BNF compliant. Since its inception, Grammatical Evolution has been used to solve real-world problems in different domains such as bio-informatics, architecture design, financial modelling, music, software testing, game artificial intelligence and parallel programming. Multi-output problems deal with predicting numerous output variables simultaneously, a notoriously difficult problem. We present a Multi-Genome Grammatical Evolution better suited for tackling multi-output problems, specifically digital circuits. The Multi-Genome consists of multiple genomes, each evolving a solution to a single unique output variable. Each genome is mapped to create its executable object. The mapping mechanism, genetic, selection, and replacement operators have been adapted to make them well-suited for the Multi-Genome representation and the implementation of a new wrapping operator. Additionally, custom grammar syntax rules and a cyclic dependency-checking algorithm have been presented to facilitate the evolution of inter-output dependencies which may exist in multi-output problems. Multi-Genome Grammatical Evolution is tested on combinational digital circuit benchmark problems. Results show Multi-Genome Grammatical Evolution performs significantly better than standard Grammatical Evolution on these benchmark problems.