Multi-objective evolution of heuristic using large language model
Heuristics are commonly used to tackle diverse search and optimization problems. Design
heuristics usually require tedious manual crafting with domain knowledge. Recent works …
heuristics usually require tedious manual crafting with domain knowledge. Recent works …
Nature-Inspired Intelligent Computing: A Comprehensive Survey
Nature, with its numerous surprising rules, serves as a rich source of creativity for the
development of artificial intelligence, inspiring researchers to create several nature-inspired …
development of artificial intelligence, inspiring researchers to create several nature-inspired …
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 …
Autoturb: Using large language models for automatic algebraic model discovery of turbulence closure
Symbolic regression (SR) methods have been extensively investigated to explore explicit
algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged …
algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged …
Llm4ad: A platform for algorithm design with large language model
We introduce LLM4AD, a unified Python platform for algorithm design (AD) with large
language models (LLMs). LLM4AD is a generic framework with modularized blocks for …
language models (LLMs). LLM4AD is a generic framework with modularized blocks for …
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
Transformer-based large language models (LLMs) have displayed remarkable creative
prowess and emergence capabilities. Existing empirical studies have revealed a strong …
prowess and emergence capabilities. Existing empirical studies have revealed a strong …
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design
Handcrafting heuristics for solving complex planning tasks (eg, NP-hard combinatorial
optimization (CO) problems) is a common practice but requires extensive domain …
optimization (CO) problems) is a common practice but requires extensive domain …
AutoTurb: Using large language models for automatic algebraic turbulence model discovery
Symbolic regression (SR) methods have been extensively investigated to explore explicit
algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged …
algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged …
[PDF][PDF] Large Language Model for Automatic Algorithm Design
Algorithm design plays a pivotal role in computational optimization and decision-making.
Traditionally, this process has been characterized by intensive trial-and-error …
Traditionally, this process has been characterized by intensive trial-and-error …