Electron Ionization (EI) libraries enable fast identification of unknowns from their mass spectra, but classical dot–product–style search falters when the observed spectrum is a superposition and chromatographic support is unavailable. We present a deconvolution workflow that relies solely on the EI–MS spectrum, requiring no external information. The method was developed with an industrial laboratory partner and evaluated on synthetic and real spectra. The method employs a two–stage strategy. First, a diagnostic–aware whitelist screens the library by rewarding agreement on strong, informative ions while penalizing unsupported intensity and maximizing over small integer m/z shifts to absorb minor misalignment. Second, a PSO-driven greedy builder assembles a sparse mixture from the shortlist, allowing a bounded per–component power stretch to accommodate modest intensity variability, followed by a brief joint refinement. We also introduce a similarity ordering of the library (cosine on log normalized spectra with optimal–leaf ordering) so that local neighbor scans probe look–alike references without altering reported indices. The fitness combines global NMSE, peak–weighted NMSE, and a spectral–angle term to emphasize diagnostic ions while preserving overall shape. On instrument–like synthetic mixtures and preliminary checks on laboratory and field EI spectra, the approach yields high–recall reconstructions. While over–selection persists in our tests, this is a reasonable trade-off given the size and redundancy of EI libraries, and preferable to missing true constituents, with settings that are straightforward to apply in practice.
Interpretability is increasingly crucial in artificial intelligence, especially in domains like medicine, where genetic programming, particularly grammatical evolution (GE), can produce models that are potentially interpretable. However, optimizing for predictive accuracy often results in complex expressions that can reduce interpretability. Large language models (LLMs) offer a promising approach to simplify and interpret complex model expressions. Here, we propose incorporating an LLM-guided local search operator into GE to enhance model interpretability in symbolic regression and test this approach for medical prediction. The operator leverages the semantic reasoning and domain knowledge encoded in LLMs to refine candidate expressions through a parallel search mechanism complementary to traditional fitness-driven evolution. Our experiments, conducted on a clinical dataset for glycated hemoglobin prediction, showed that three LLM-guided variants improve interpretability compared to baseline GE, with two showing signs of possible overfitting and the other generalizing better. LLM-generated individuals demonstrated evolutionary viability throughout evolution and direct contribution to the final solutions. Comparison of proprietary and local LLMs revealed challenges with adherence to instructions and creative exploration in smaller models. These results indicate that the proposed approach is a promising strategy for obtaining simpler, more interpretable, and more plausible models.
Evolutionary computation (EC) suffers from a lack of standardized open-source frameworks, the need of domain-specific configurations for every new problem, and missing general guidelines for choosing appropriate solution methods. Unlike other areas of artificial intelligence (AI), such as machine learning, which provide ready-to-use programming libraries with reusable components, this degree of standardization is less pronounced in EC. In this paper, a vision for the next stage of EC is proposed, introducing an architecture for an agentic generative AI system designed to unify and automate EC processes, enabling higher levels of integration, autonomy, and self-organization. This represents a step toward a new paradigm for interacting with agentic and generative AI, supporting dynamic, multi-agent collaboration in which problem modeling, algorithmic design, and efficient computation are seamlessly integrated across the entire EC ecosystem. Ultimately, this could reduce the entry barrier and foster a broader adoption of EC.
Classical competitive coevolution has long utilized subjective measurements of historical progress and post hoc competition to quantify results due to intractable search spaces. This has contributed to divergent perspectives and disjointed literature, which pose an obstacle to new researchers and practitioners. In response to a point of confusion in literature, this work investigates the potential for the Hall of Fame technique to produce deceptive fitness approximation effects in competitive coevolution and draws a through line between disconnected literature. Using a parameterized number game with variable dimensions of intransitivity and a predator-prey meta-game, we conduct an investigation of fitness approximation during competitive coevolution using fully-enumerated search spaces to establish objective ground truth. We observe that fitness approximations during competitive coevolution can result in deceptive solution ranking that produces breakdowns in elitism and global search progress, as theorized in literature, and objectively quantify this effect for the first time. We identify that these effects are directly related to the presence of intransitivity and show that deceptive effects increase with Hall of Fame use, but also with increasing dimensions of intransitivity and high selective pressure in survival selection.
The growing deployment of Electric Vehicles (EVs) presents critical operational challenges, particularly in assigning limited charging infrastructure while ensuring user satisfaction. This work addresses the Electric Vehicle Charging Scheduling Problem (EVCSP) to minimize total tardiness, i.e., the cumulative delay beyond EVs’ intended departure times. We consider a centralized, coordinated, and offline scheduling setting, involving heterogeneous, non-preemptive, fixed-capacity chargers. A Mixed-Integer Linear Programming (MILP) formulation is first developed to model charger assignments and scheduling constraints. Given the computational complexity for solving large instances to optimality, we introduce a greedy, priority-based heuristic and a General Variable Neighborhood Search (GVNS) metaheuristic. The heuristic uses a tailored urgency index to assign vehicles rapidly, while GVNS explores multiple neighborhoods and local optima to enhance solution quality. Both approaches are benchmarked using synthetic scenarios with different fleet sizes, time-window tightness, and charger configurations. Results show that GVNS consistently outperforms the heuristic and the exact model in larger instances, achieving near-optimal solutions within short runtimes. The proposed hybrid methodology provides a scalable and practical solution for EV fleet operators seeking efficient, real-time-ready scheduling under infrastructure constraints.
