The paper introduces a dual‑perspective explainability framework for Particle Swarm Optimization (PSO). From a landscape viewpoint, it uses Exploratory Landscape Analysis (ELA) and machine‑learning classifiers to predict topology‑specific hyperparameters for unseen problems. From an algorithmic viewpoint, it incorporates IOHxplainer and Search Trajectory Networks (STN) with new metrics—Connectivity Density, Fragmentation Score, and Search Efficiency—to visualize and quantify PSO’s search organization and transition effectiveness across 24 benchmark functions and multiple topologies.
arXiv:2607. 19031v1 Announce Type: new Abstract: Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels.
arXiv:2608.23323v1 Announce Type: cross Abstract: Continuous optimisation methods need to balance sharing information and maintaining alternative search directions. In this paper, we introduce Myceli...
arXiv:2505. 15998v4 Announce Type: replace Abstract: We present a curiosity-driven AI scientist method for discovering system-level dynamics in Flow-Lenia, a continuous cellular automaton (CA) with mass conservation and parameter localization.
arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?
arXiv:2606. 30836v1 Announce Type: new Abstract: Explainability in Many-Objective Optimization (MaO) is currently hindered by the escalating complexity of the Pareto front, which renders the relationship between high-dimensional decision variables and objective outcomes increasingly opaque.
arXiv:2607. 09971v1 Announce Type: new Abstract: Archive-based exploration methods such as Go-Explore select which visited state to return to using visitation rarity, and frontier methods return to the boundary of the unknown; neither asks whether the unexplored region behind a boundary is enterable at all.
The paper introduces LLM-EBG, an evolutionary framework that uses a large language model as a generative operator to automatically create optimization benchmarks. By generating unconstrained single-objective continuous minimization problems expressed as mathematical formulas, the framework can produce benchmarks that consistently favor a target algorithm over a comparison algorithm in over 80% of trials. Landscape analysis shows that these generated problems exhibit distinct geometric traits, such as sensitivity to variable scaling, reflecting the search behaviors of different optimization methods.
arXiv:2606. 07557v1 Announce Type: new Abstract: Decentralized multi-agent swarm coordination on resource-constrained edge platforms remains fundamentally bottlenecked by the exponential scaling of joint action spaces and high-latency communication overhead.
arXiv:2607. 24647v1 Announce Type: new Abstract: AI-driven autonomous research (AR) systems are becoming increasingly effective across a broad range of tasks.
arXiv:2502. 08397v3 Announce Type: replace-cross Abstract: Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together.
arXiv:2606. 00366v1 Announce Type: new Abstract: We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems.
arXiv:2606. 02863v1 Announce Type: new Abstract: AI-Driven Research Systems (ADRS) -- systems coupling LLMs with automated evaluation to discover algorithms, proofs, and designs -- are being optimized and adopted across domains, but the tools to analyze them have not kept pace.