Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization
In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging
avenue within the Evolutionary Computation (EC) community, which incorporates Meta …
avenue within the Evolutionary Computation (EC) community, which incorporates Meta …
A systematic survey on large language models for algorithm design
Algorithm Design (AD) is crucial for effective problem-solving across various domains. The
advent of Large Language Models (LLMs) has notably enhanced the automation and …
advent of Large Language Models (LLMs) has notably enhanced the automation and …
Artificial evolutionary intelligence (AEI): evolutionary computation evolves with large language models
Deep learning (DL) and evolutionary computation (EC), two main branches of artificial
intelligence, have attracted attention in a far different way over the past decades. On the one …
intelligence, have attracted attention in a far different way over the past decades. On the one …
Trace is the next autodiff: Generative optimization with rich feedback, execution traces, and llms
We study a class of optimization problems motivated by automating the design and update
of AI systems like coding assistants, robots, and copilots. AutoDiff frameworks, like PyTorch …
of AI systems like coding assistants, robots, and copilots. AutoDiff frameworks, like PyTorch …
[PDF][PDF] Code Comment Inconsistency Detection and Rectification Using a Large Language Model
G Rong, Y Yu, S Liu, X Tan, T Zhang… - 2025 IEEE/ACM 47th …, 2024 - people.cs.umass.edu
Comments are widely used in source code. If a comment is consistent with the code snippet
it intends to annotate, it would aid code comprehension. Otherwise, Code Comment …
it intends to annotate, it would aid code comprehension. Otherwise, Code Comment …
LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
Optimization problems are prevalent across various scenarios. Formulating and then solving
optimization problems described by natural language often requires highly specialized …
optimization problems described by natural language often requires highly specialized …
Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning
Achieving the effective design and improvement of reward functions in reinforcement
learning (RL) tasks with complex custom environments and multiple requirements presents …
learning (RL) tasks with complex custom environments and multiple requirements presents …
[PDF][PDF] Design and Optimization of Heat Exchangers Using Large Language Models
Heat exchangers (HEs) are essential in process industries for efficient thermal energy
transfer. Their design and optimization are crucial for improving energy efficiency, reducing …
transfer. Their design and optimization are crucial for improving energy efficiency, reducing …
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
Recent advances in Meta-learning for Black-Box Optimization (MetaBBO) have shown the
potential of using neural networks to dynamically configure evolutionary algorithms (EAs) …
potential of using neural networks to dynamically configure evolutionary algorithms (EAs) …
LLM Cascade with Multi-Objective Optimal Consideration
Large Language Models (LLMs) have demonstrated exceptional capabilities in
understanding and generating natural language. However, their high deployment costs …
understanding and generating natural language. However, their high deployment costs …