Evolutionary Optimization

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Evolutionary optimization studies how populations of candidate solutions can improve through selection, mutation, recombination, and other forms of variation, especially when gradients are unavailable or objectives are expensive. Common approaches include genetic algorithms, differential evolution, CMA-ES and evolution strategies, as well as multi-objective methods that preserve trade-offs and diversity. Researchers apply them to engineering design, robotics, scheduling, scientific discovery, and increasingly to evolving code, prompts, and language-model behavior; newer methods add learned surrogates, adaptive resource allocation, and large-language-model-generated operators. A central challenge is balancing exploration, solution quality, and evaluation cost, while ensuring improvements generalize beyond the benchmarks and search budgets used to find them.

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295 papers33 papers in the last four weeks, up 50% on the four weeks before. 0.2% of all new papers.

Holistic Diversity Management in LLM-Guided Evolutionary Search
Oct 28, 2026Seockbean Song, Youngsik Yoon, Sungjae Lee +3Evolutionary Optimization LLMs for Evolutionary Optimization
Graduate School of AI, POSTECH · Department of CSE, POSTECH · Microsoft Research Asia
WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization
Oct 27, 2026Wenjie Qiu, Hepei Li, Jianfeng Wang +2Global Optimization Evolutionary Optimization
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China · Taiyuan University of Technology, Taiyuan 030024, China · Nankai University, Tianjin 300350, China, and also with Hanyang University ERICA, Ansan 15588, South Korea
Hyperparameter and Neural Architecture Search via Evolutionary Optimization for Reinforcement Learning: HPNAS-RL
Oct 27, 2026Nihal Acharya Adde, Hanno GottschalkNeural Architecture Search Reinforcement Learning
Volkswagen Group Innovation, Volkswagen AG, Berliner Ring 2, Wolfsburg, 38440, Lower Saxony, Germany. · Institute of Mathematics, TU Berlin, Straße des 17. Juni 136, Berlin, 10623, Berlin, Germany.
Center-of-Mass Interpretation of Evolution Strategies
Oct 26, 2026Akshat GuptaEvolution Strategies Evolutionary Optimization
UC Berkeley
Understanding the Co-evolutionary Solution Exchange of the MOEA/D: Already the Smallest Communication Radius Gives the Best-Possible Speed-Up
Oct 26, 2026Benjamin Doerr, Moaad Elmoutassim, Martin S. Krejca +1Multi-Objective Evolutionary Optimization Evolutionary Optimization
Laboratoire d’Informatique (LIX), CNRS, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France · École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
Lineage-Aware Search Control for LLM-Based Evolutionary Factor Mining
Oct 21, 2026Xintong Liu, Chenkai Zhang, Yanzhao Ma +1Alpha Factor Discovery LLMs for Evolutionary Optimization
Southern University of Science and Technology (SUSTech) Shenzhen, China
ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
Oct 21, 2026Yongqiang Chen, Chenxi Liu, Deyu Zou +4AI Agents for Scientific Discovery Large Language Model-Guided Scientific Discovery
MBZUAI · Carnegie Mellon University · TMLR Group, Hong Kong Baptist University +2
Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization
Oct 21, 2026Dominik Jehle, Tom ZehleMulti-Agent System Optimization Multi-Agent Systems
University of Freiburg Freiburg im Breisgau, Germany
LLM Evolution in a Simple Simulated Market
Oct 16, 2026Charlie Pilgrim, Christopher Krupenye, Dora Biro +1Emergent Misalignment in Language Models Self-Evolving Agents
University of Leeds · Johns Hopkins University · University of Rochester
Algorithm Modelization: Transforming Optimization Algorithms into Evolvable Intelligence
Oct 16, 2026Jian Yao, Zhiyao Zhang, Yichen Li +2Learning to Optimize Algorithmic Generalization
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Evo-B&B: Behavior-Aware LLM-Guided Joint Evolution of Branching and Node Selection Policies for MILP
Oct 16, 2026Jiayu Chen, Chentong Chen, Yuhan Liu +3Branch and Bound Mixed-Integer Linear Programming
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China · School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China
A restart rule for genetic algorithms based on Schnabel census
Oct 15, 2026Anton Eremeev, Yulia ZakharovaGenetic Algorithms Evolutionary Optimization
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills
Oct 14, 2026Enze Ma, Chunyu Miao, Yankai Chen +14LLM Agent Skill Learning LLM Agent Self-Improvement
University of Illinois at Chicago · Mohamed bin Zayed University of Artiﬁcial Intelligence · Independent Researcher +3
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design
Oct 12, 2026Oguzhan Gungordu, Ayse Tursucular, Siheng Xiong +1Automated Heuristic Design Large Language Model-Guided Optimization
Georgia Institute of Technology
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming
Oct 11, 2026Abrar Alotaibi, Moataz AhmedAdversarial Prompt Generation Adversarial Attacks on LLMs
King Fahd University of Petroleum & Minerals, Information and Computer Science Department, Dhahran, Saudi Arabia · Imam Abdulrahman Bin Faisal University, College of Computer Science and Information Technology, Dammam, Saudi Arabia · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
DiffGA: Steering Frozen Diffusion Models for Materials Design via Evolutionary Search
Oct 10, 2026Shivang Agarwal, Rodrigo Wang, Kevin Ryczko +1Inverse Design Crystal Structure Generation
SandboxAQ, Palo Alto, CA 94301, USA.
Stride: Scale-Aware Multi-Agent Evolutionary Refinement for Infographic Generation
Oct 9, 2026Yan Zhou, Yang YangAgentic Image Generation Controllable Image Generation
School of Artificial Intelligence Shanghai Jiao Tong University
Meta-Reflective Evolution Effectively Improves Production AI Systems
Oct 8, 2026Seung Won Wilson Yoo, Chang Zeng, Aditi Somayajula +7Large Language Model-Guided Optimization Evolutionary Optimization
Snowflake · UMass Amherst · Stanford +2
EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution
Oct 8, 2026Yanning Dai, Yuhui Wang, Nanbo Li +2Large Language Model-Guided Optimization Self-Evolving Agents
Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. · Sakana AI, Tokyo, Japan.
Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating
Oct 8, 2026Lechen Li, Rongye Shi, Wanhuan ZhouEvolutionary Optimization Model Calibration
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 519000, China · College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China · School of Artificial Intelligence, Beihang University, Beijing 100191, China
EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs
Oct 7, 2026Leizhen Wang, Peibo Duan, Zhenlin Qin +5Traffic Signal Control Automated Heuristic Design
Monash University · KTH Royal Institute of Technology · Chinese Academy of Sciences +2
Discovered, Not Designed: Population Evolution for Collaborative and Compute-Intensive Model Discovery
Oct 5, 2026Bo Peng, Lizhu Zhang, Yuhang Zhou +5Automated Algorithm Discovery Evolutionary Optimization
Meta
$\mathrm{TRIZ}^{a}$ : Guiding Agent Evolution from Pattern Recognition to Solution Invention
Oct 3, 2026Wenyin Liu, Yiheng Huang, Kai WangAutomated Algorithm Discovery Self-Evolving Agents
Guangdong University of Technology · Beijing University of Posts and Telecommunications · Beijing Denglu Technology Ltd
FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution
Oct 2, 2026Hui Chen, Xuan Qi, James Xu Zhao +5Large Language Model-Guided Optimization Evolutionary Optimization
National University of Singapore · University of Washington · Princeton University +1
Multi-Task Evolution for Zero-Shot Cross-Problem Generalization using LLMs
Oct 2, 2026Zhuoliang Xie, Changliang Zhou, Genghui Li +1Automated Heuristic Design Large Language Model-Guided Optimization
School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China · Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology, Shenzhen, China · College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Distribution Matching Evolutionary Algorithms for Rare Event Sampling
Oct 2, 2026Yonatan Gideoni, Yarin GalDistribution Matching Evolutionary Optimization
University of Oxford
SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents
Oct 1, 2026Amirhossein Abaskohi, Mahdi Mostajabdaveh, Zirui ZhouMemory-Augmented Agents LLMs for Evolutionary Optimization
University of British Columbia · Huawei Technologies Canada
VideoEvolve: Evolving Agent Harnesses for Video Temporal Grounding
Oct 1, 2026Bingjun Luo, Yuhuan Fan, Jialin Guo +1Temporal Video Grounding Evolutionary Optimization
Tsinghua University · Columbia University · Harbin Engineering University
LESS: Lightweight Evolutionary Supernet Search in Minutes
Oct 1, 2026Aviral Gandhi, Jinglue Xu, Jialong Li +1Evolutionary Optimization Covariance Matrix Adaptation Evolution Strategy
The University of Tokyo · Sakana AI · Waseda University
Self-Evolving Coding Rules for AI Coding Agents
Sep 30, 2026Zhengyuan Jiang, Reachal Wang, Yuepeng Hu +3Evolutionary Optimization Coding Agents
Duke University

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Holistic Diversity Management in LLM-Guided Evolutionary Search

Seockbean Song, Youngsik Yoon, Sungjae Lee, Siwei Wang+2

Graduate School of AI, POSTECH · Department of CSE, POSTECH · Microsoft Research Asia

Evolutionary Optimization LLMs for Evolutionary Optimization Diversity-Aware Sampling

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LLM-guided evolutionary search iteratively selects existing programs, uses LLMs as mutation operators to generate a new program, and evaluates it to guide subsequent search. To preserve diversity and reduce the risk of premature convergence, it often adopts the island model, in which groups of programs, called islands, evolve separately. However, this structure alone does not guarantee distinct search directions, as islands may start from similar programs or converge toward similar solutions during evolution. This suggests that diversity must be considered throughout the search process, from initializing islands with distinct programs to developing island trajectories and sharing discoveries across islands. To this end, we propose HoliDivEvolve , a holistic approach to establishing and effectively utilizing island diversity throughout evolutionary search. Specifically, we introduce diverse initialization to establish different initial programs, trajectory adaptive island selection to allocate computation based on current score, recent growth, and the remaining search budget, and growth adaptive migration to share alternative ideas across islands. Experiments across diverse benchmarks show that HoliDivEvolve achieves competitive performance on most evaluated tasks. We further conduct controlled analyses to examine how our design choices affect search behavior and final performance.