A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Yisong Zhang, Ran Cheng, Guoxing Yi, Kay Chen Tan
cs.NE, cs.AI
Submitted: 2026-08-19
Updated: 2026-08-20
Comments: Accepted by IEEE CIM
Code: https://github.com/ishmael233/LLM4OPT
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
- Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
- A Survey of Large Language Models
- PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model
- BloombergGPT: A Large Language Model for Finance
- Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
- Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
- Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms
- When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges
- A Systematic Survey on Large Language Models for Algorithm Design
- Synthesizing mixed-integer linear programming models from natural language descriptions
- Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
- OPD@NL4Opt: An ensemble approach for the NER task of the optimization problem
- VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition
- A Novel Approach for Auto-Formulation of Optimization Problems
- Towards an Automatic Optimisation Model Generator Assisted with Generative Pre-trained Transformer
- Holy Grail 2.0: From Natural Language to Constraint Models
- TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners
- OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models
- OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
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