arXiv:2606. 28644v1 Announce Type: cross Abstract: Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation.
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The paper critiques the common practice of evaluating large‑language‑model (LLM) evolutionary search methods using a single seed and fixed iteration budget, arguing that this approach is insufficient. By testing three search strategies across five optimization tasks and varying both the number of seeds (width) and iterations (depth), the authors find that optimal budget allocation depends on the strategy, task, and total budget, and that strategy rankings shift with different budgets. They propose a measurement protocol that maps the seeds‑by‑iterations frontier and offers practical guidance for researchers.
arXiv:2607. 29228v1 Announce Type: cross Abstract: Swarm and evolutionary algorithms are usually analyzed as complete procedural systems in which nonlinear selection, replacement, and adaptation obscure simpler structure within candidate generation.
arXiv:2608. 11258v1 Announce Type: new Abstract: Gradient injection helps Particle Swarm Optimization (PSO) only when the swarm has identified a basin with smooth local structure, not universally.
The paper introduces the Coronavirus Optimization Algorithm (COA), a SARS‑CoV‑2 inspired evolutionary optimizer for box‑constrained continuous global optimization. COA maps coronavirus mechanisms to search operators such as elite‑guided attraction, trial‑vector generation, adaptive parameter variation, stagnation recovery, and population‑size scheduling, and incorporates opposition‑based initialization, current‑to‑pbest mutation, binomial crossover, an external archive, success‑history adaptation, population reduction, and partial restart. Evaluated on 29 CEC 2017 benchmark functions at 10, 30, and 50 dimensions, COA achieves the best overall Friedman rank across all dimensions, excelling particularly on composition functions while noting limitations on some hybrid functions and the need for further high‑dimensional validation.
arXiv:2606. 12382v1 Announce Type: cross Abstract: The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is a popular and prominent evolutionary algorithm for solving multi-objective optimisation problems.
arXiv:2608. 02073v1 Announce Type: cross Abstract: We investigate Optimization under Input Uncertainty (OIU), in which the input to the objective function, rather than the objective function itself, is subject to uncertainty.