Jul 2026· Annual Conference on Genetic and Evolutionary Computation· pp. 948-956· 0 citations· 39 references
Computer Science
TL;DR
This paper proposes to represent complete execution trajectories as graphs and learn their embeddings using graph neural networks and shows that the learned embeddings can effectively distinguish between algorithms even when evaluated across diverse benchmark functions.
Abstract
Optimization algorithms are traditionally represented by their descriptions, source code, and comparative results in benchmark environments. In recent years, however, it has become more popular to extract numerical features describing the behavior of algorithms during their operation. Such metrics can distinguish between algorithms, assess their novelty, or assist in selecting the best algorithm for a given problem. Unlike existing trajectory-based approaches that use partial trajectory information to derive hand-crafted behavioral features, we propose to represent complete execution trajectories as graphs and learn their embeddings using graph neural networks. In this paper, we explore two graph construction schemes and enrich graph vertices with evaluation and topological features. We compare the proposed approach with existing behavioral features and introduce additional experiments demonstrating the utility of behavioral analysis. Through extensive experiments, we show that the learned embeddings can effectively distinguish between algorithms even when evaluated across diverse benchmark functions. We introduce quantitative measures to assess how configuration influences behavioral expression and demonstrate that partial trajectory features can predict optimization success, enabling informed early stopping decisions. The resulting embedding space provides a foundation for constructing diverse algorithm portfolios, evaluating parameter significance, and enabling future analyses of relationships between problem characteristics and algorithm performance.
This work-in-progress paper proposes graph edit paths as a controlled tool for probing how GNN predictions respond to structural and attribute-level edits, and develops heuristics for generating edit paths and evaluates standard GNNs along these paths, quantifying their sensitivity to different types of edits.
Search Trajectory Networks (STNs) are a graph-based tool for visualizing and characterizing the behavior of optimization algorithms. STNs'reliance on discretization of the search space has largely confined them to low-dimensional or combinatorial settings. We introduce a methodology for constructing STNs in semantic spaces, defined as the space of a model's predictions on a fixed sample set. Our approach discretizes semantic vectors and aggregates them into network nodes via agglomerative clustering with complete linkage under a normalized Hamming distance. Since any predictor can be summarized by its semantic vector, this method enables comparison of learning dynamics across otherwise incomparable algorithm families. We apply semantic space STNs to classification and regression tasks solved using different machine learning algorithms, recovering known qualitative differences between them. Additionally, we use semantic space STNs to study neural network generalization by contrasting standard training with the label randomization regime of Zhang et al. (2017). The resulting STNs exhibit consistent structural differences, training on real labels produces denser, more efficient and more centralized graphs than training on shuffled labels. Together, our results show that semantic space STNs capture functional training dynamics arising from the interaction between learning algorithms and data, providing a tool for analyzing and comparing learning dynamics across machine learning models and training regimes.
J. Agudelo, A. Tonda, Gabriela Ochoa et al.· 0 citations
Ensemble-GNAS is proposed, a novel framework that integrates evolutionary GNAS with ensemble learning to address limitations of existing graph NAS algorithms and outperforms the state-of theart methods in both classification accuracy and performance stability.
Weifeng Guo, Pengyu Wang, Ying Bi et al.· IEEE Transactions on Neural...· 0 citations
A pairwise surrogate model is introduced that predicts whether an offspring architecture is likely to outperform its parent using genotype-derived features and early training signals, enabling full evaluation only for promising candidates within the evolutionary loop.
Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi· GECCO Companion· 0 citations
This survey comprehensively analyzes contemporary symbolic regression methodologies by systematically integrating four paradigmatic approaches: deterministic methods, metaheuristic algorithms, neural-symbolic frameworks, and emerging hybrid strategies, establishing a unified taxonomic framework that bridges evolutionary computation, mathematical programming, and deep learning paradigms.
Vikas Palakonda, Samira Ghorbanpour, Sangseok Yun et al.· Archives of Computational Me...· 0 citations
Evolutionary program search guided by Large Language Models (LLMs) has emerged as a powerful paradigm for automated scientific discovery. However, current approaches are fundamentally constrained by three bottlenecks: structurally blind parent selection, sparse whole-program evaluation rewards, and static mutation operators that fail to adapt during search. We present GAE (Graph-Augmented Evolution), a framework that resolves these limitations through a tightly coupled, three-pillar architecture. First, a relational graph neural network (GNN) parses programs into typed computation graphs, producing structure-aware embeddings. Second, an RL-optimized meta-controller leverages these embeddings to replace blind evolutionary sampling with a directed policy, dynamically selecting optimal parents and mutation directions based on reward history. Third, an online GRPO fine-tuning loop continuously updates the LLM mutation operator at test-time using group-normalized evaluation rewards, directly aligning the model's generation distribution with high-fitness structural edits. We evaluate GAE on a challenging scientific discovery task: symbolic regression for complex nonlinear oscillator systems. By transforming stochastic search into a directed, self-improving trajectory, GAE efficiently discovers closed-form physical equations, consistently matching or outperforming static LLM-driven baselines and achieving state-of-the-art out-of-distribution performance.
Xuanzhou Chen, Taoli Cheng· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
