LLMs for Evolutionary Optimization

LLM: Large Language Model

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41 papers14 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search
Apr 21, 2026Xinhao Zhang, Xi Chen, François Portet +1Evolutionary Optimization Large Language Model-Guided Optimization
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France
TurboEvolve: Towards Fast and Robust LLM-Driven Program Evolution
Apr 12, 2026Yang Yang, Zining Zhong, Jindong Li +5Large Language Model-Guided Optimization Evolutionary Optimization
Artificial Intelligence Thrust, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China.
RoboPhD: Evolving Diverse Complex Agents Under Tight Evaluation Budgets
Apr 6, 2026Andrew Borthwick, Stephen Ash, Anthony GalczakLLMs for Evolutionary Optimization Large Language Model-Guided Optimization
Independent Researchers
CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery
Apr 1, 2026Youssef Mroueh, Carlos Fonseca, Brian Belgodere +1LLMs for Evolutionary Optimization Automated Algorithm Discovery
IBM Research
EvoX: Meta-Evolution for Automated Discovery
Feb 26, 2026Shu Liu, Shubham Agarwal, Monishwaran Maheswaran +14Evolutionary Optimization LLMs for Evolutionary Optimization
UC Berkeley · Stanford University · Bespoke Labs
Code World Models for Parameter Control in Evolutionary Algorithms
Feb 25, 2026Camilo Chacón Sartori, Guillem Rodríguez CorominasCode World Models Large Language Model-Guided Optimization
ICN2 – Catalan Institute of Nanoscience and Nanotechnology, Barcelona, Spain · Artificial Intelligence Research Institute (IIIA-CSIC), Barcelona, Spain · Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization
Feb 23, 2026Mert Cemri, Shubham Agrawal, Akshat Gupta +9Large Language Model-Guided Optimization LLMs for Evolutionary Optimization
University of California, Berkeley · Bespoke Labs
Simple Baselines are Competitive with Code Evolution
Feb 18, 2026Yonatan Gideoni, Sebastian Risi, Yarin GalLLMs for Evolutionary Optimization Code Generation Evaluation
University of Oxford · Sakana AI and ITU Copenhagen
ImprovEvolve: Basin-Hopping Meets LLM-Guided Evolutionary Search
Feb 10, 2026Alexey Kravatskiy, Valentin Khrulkov, Ivan OseledetsLarge Language Model-Guided Scientific Discovery Evolutionary Optimization
1MIRIAI, Russia · 2FusionBrain Lab, Russia · 3AXXX, Russia +1
EvoDR: Evolving Dispatching Rules via Large Language Model for Dynamic Flexible Assembly Flow Shop Scheduling
Jan 22, 2026Junhao Qiu, Haoyang Zhuang, Fei Liu +2Automated Heuristic Design LLMs for Evolutionary Optimization
Department of Computer Science, City University of Hong Kong, Hong Kong, China. · School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China.
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Sep 10, 2025Yisong Zhang, Ran Cheng, Guoxing Yi +1Evolutionary Optimization Large Language Model-Guided Optimization

Apr 21, 2026J/K move · Enter open · S save

What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search

Xinhao Zhang, Xi Chen, François Portet, Maxime Peyrard

Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France

Evolutionary Optimization Large Language Model-Guided Optimization LLMs for Evolutionary Optimization

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Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these optimization gains remain poorly understood. In this work, we present a large-scale study of LLM-guided evolutionary search, collecting optimization trajectories for 15 LLMs across 8 tasks. Although zero-shot problem-solving ability correlates with final optimization outcomes, it explains only part of the variance: models with similar initial capability often induce dramatically different search trajectories and outcomes. By analyzing these trajectories, we find that strong LLM optimizers behave as local refiners, producing frequent incremental improvements while progressively localizing the search in semantic space. Conversely, weaker optimizers exhibit large semantic drift, with sporadic breakthroughs followed by stagnation. Notably, various measures of solution novelty do not predict final performance; novelty is beneficial only when the search remains sufficiently localized around high-performing regions of the solution space. Our results highlight the importance of trajectory analysis for understanding and improving LLM-based optimization systems and provide actionable insights for their design and training.