arXiv AI
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.
The paper evaluates the Tabular Prior-data Fitted Network (TabPFN) as a surrogate model in surrogate‑assisted evolutionary algorithms (SAEAs) for expensive optimization problems. Through extensive experiments in both offline and online settings across a range of problem types—including single‑objective, multi‑objective, constrained, combinatorial, mixed‑variable, and engineering tasks—the study finds that TabPFN’s effectiveness varies strongly with the problem characteristics. The authors conclude that TabPFN should be used selectively, with customized model management and algorithm design tailored to data availability, landscape complexity, and search‑space properties.
By Lu Han, Jin Wang, Yuchen Li, Haoran Gu, Shulei Liu, Ziyang Shi, Wenao Lu, Handing Wang
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du
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.
By Yuhiro Ono, Tomohiro Harada, Yukiya Miura
arXiv:2606. 07562v1 Announce Type: cross Abstract: RNA design consists of discovering a nucleotide sequence that optimizes predefined criteria, such as secondary structure.
By Tristan Cazenave
arXiv:2606. 15363v1 Announce Type: new Abstract: Self-improvement in AI agents has emerged as a key research frontier: systems that modify their own prompts, workflows, and decision rules based on accumulated operational experience.
By Ya-Chuan Chen, Tien-Jen Lai, Hsiang-Wei Hu
arXiv:2602. 10233v2 Announce Type: replace-cross Abstract: LLM-guided evolutionary computation, most notably AlphaEvolve, has been remarkably successful in discovering novel mathematical constructions by solving challenging optimization problems.
By Alexey Kravatskiy, Valentin Khrulkov, Ivan Oseledets
arXiv:2607. 23408v1 Announce Type: new Abstract: Expensive black-box optimization is ubiquitous in science and engineering, where function evaluations are costly and the evaluation budget is limited.
By Jintao He, Huixiang Zhen, Wenyin Gong
arXiv:2607. 24647v1 Announce Type: new Abstract: AI-driven autonomous research (AR) systems are becoming increasingly effective across a broad range of tasks.
By Haiqian Yang, Yuan Cao
arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
By Benno Kaech, Luis Wyss, Karsten Borgwardt, Gianvito Grasso
The paper introduces Mol-E, an evolutionary algorithm that leverages large language models trained on molecular data to generate candidate molecules. Mol-E achieves state‑of‑the‑art performance on the Practical Molecular Optimization benchmark, scoring 17.500 in the task‑agnostic regime and 20.551 in the task‑informed regime. It also outperforms baseline methods in multi‑property optimization tasks involving docking against DRD2, MK2, and AChE.
By Philipp Guevorguian, Menua Bedrosian, Tigran Fahradyan, Gayane Chilingaryan, Armen Aghajanyan, Hrant Khachatrian
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:2609.40340v1 Announce Type: new
Abstract: Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks. Supplying relevant documents...
By Young-Jun Lee, Jinheon Baek, Soyeong Jeong, Minki Kang, Seungyeon Jwa, Jonghyun Choi, Seungho Han, Dongyeop Kang