arXiv:2606. 14289v1 Announce Type: cross Abstract: Population-based and distributional optimization methods, from evolution strategies and consensus-based optimization to covariance-matrix adaptation and stochastic gradient methods viewed as distributional dynamics, are widely used for nonconvex or black-box problems, yet their convergence analyses remain fragmented across algorithm-specific techniques.
By Pekka Malo, Lauri Viitasaari, Patrik Nummi, Antti Suominen, Ankur Sinha, Olli Tahvonen
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:2606. 12279v1 Announce Type: cross Abstract: Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems.
By Anna Brandenberger, Ilan Doron-Arad, Elchanan Mossel
The paper introduces a new framework for genetic algorithms where mutation and recombination operators are guided by machine‑learning optimization rather than random changes. It shows that such operators can improve objective values but at higher computational cost, and demonstrates three key phenomena: the necessity of solution‑pool diversity for parity learning, the simultaneous need for generation, mutation, and recombination to achieve near‑optimal solutions, and a phase transition in Gaussian settings where positive drift yields exponential speedup.
By Anna Brandenberger, Ilan Doron-Arad, Elchanan Mossel
arXiv:2608. 08156v1 Announce Type: new Abstract: In evolutionary algorithms powered by language models, the LLM acts as a single operator that simultaneously updates structural components (like control flow) and continuous parameters.
By V\'ictor Gallego
arXiv:2609.13396v1 Announce Type: new
Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In M...
By Chao Jiang, Yueling Huang, Miqing Li
The paper presents high‑probability generalization guarantees for Randomized Block Coordinate Descent (RBCD) when applied to nonconvex learning problems. It derives bounds on the gradients of both empirical and population risks under standard smoothness assumptions, and shows how polynomially decaying step sizes and early stopping can achieve optimal convergence rates for population gradients. In settings where the objective is gradient‑dominated, the authors provide functional suboptimality bounds that approach optimistic rates seen in convex or low‑noise scenarios, and they also establish dimension‑independent bounds for loss functions with a particular structure, extending applicability to high‑dimensional problems.
By Ruixi Hu, Liang Wu
arXiv:2606. 07841v1 Announce Type: cross Abstract: Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization.
By Trevor Campbell, Jonathan H. Huggins, Kyurae Kim, Charles C. Margossian
The paper revisits a theory linking sexual reproduction to the multiplicative weights update algorithm (MWUA), originally proposed by Chastain et al. It removes the earlier assumptions of weak selection and product distribution, showing that marginal allele distributions from population dynamics match those produced by MWUA in a general setting. The authors also revise conclusions about convergence and fitness guarantees in coordination games, arguing that sexual dynamics do not impose additional properties on the population distribution beyond convergence.
By Reshef Meir, David Parkes
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:2609.05992v1 Announce Type: new
Abstract: As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task complet...
By Hengle Jiang, Qijun Cai, Ziying Luo, Ke Tang
T‑GADE is an evolutionary computation framework that integrates large language models (LLMs) to generate and evolve structured artifacts. It retains classic population and genetic operators while using LLMs for crossover, mutation, and a self‑repair operator called HoxLM. The framework introduces temperature‑controlled free‑energy selection and occupancy rules, and demonstrates improved performance on an online bin‑packing task compared to a baseline Evolution of Heuristics approach.
By Kyoko Ogawa, Naoki Mori