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.
By Tal Oved, Roi Pony, Oshri Naparstek, Udi Barzelay
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.
By Vojt\v{e}ch Nov\'ak, Ivan Zelinka
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.
By Aryan Gurudeo
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.
By Hari Mohan Pandey
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.
By Duc-Cuong Dang, Andre Opris, Dirk Sudholt
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.
By So Nakashima, Tetsuya J. Kobayashi
arXiv:2609.13533v1 Announce Type: cross
Abstract: Most hyperparameter configurations for Evolvable-Substrate HyperNEAT (ES-HyperNEAT) produce networks that stagnate at random-guessing performance, wa...
By Romain Claret, Arthur Gygax, Michael O'Neill, Paul Cotofrei, Pascal Felber
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...
By Mohammad Mahdi Dehshibi
arXiv:2609.13247v1 Announce Type: cross
Abstract: Calibrating an agent-based model (ABM) is difficult because its objective landscape is stochastic and rugged, and can be evaluated only through costl...
By Duguma Yeshitla Habtemariam, Jihwan Lee
We’ve discovered that evolution strategies (ES), an optimization technique that’s been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks (e. g.
arXiv:2608.21995v1 Announce Type: cross
Abstract: We propose Variance Driven Exploration (VarDE), a principled approach for pure exploration in highly stochastic environments, where the exploration p...
By Khang Luong, Nam Nguyen, Hoang Ta, Hung The Tran, Tuan Dam
arXiv:2609.00129v1 Announce Type: cross
Abstract: The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem they were trained on drifts. This is a nea...
By J. M. Diederik Kruijssen (Allora Foundation)