1 citations · 1 across the 5 of their papers we have counts for
5 papers · 1 filter
GEAKG: Generative Executable Algorithm Knowledge Graphs
Camilo Chacón Sartori, José H. García, Andrei Voicu Tomut +1
In the context of algorithms for problem solving, procedural knowledge -- the know-how of algorithm design and operator composition -- remains implicit in code, lost between runs,…
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms
Camilo Chacón Sartori, Christian Blum
Large Language Models (LLMs) have shown notable potential in code generation for optimization algorithms, unlocking exciting new opportunities. This paper examines how LLMs, rather…
Improving Existing Optimization Algorithms with LLMs
Camilo Chacón Sartori, Christian Blum
The integration of Large Language Models (LLMs) into optimization has created a powerful synergy, opening exciting research opportunities. This paper investigates how LLMs can enha…
Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach
Camilo Chacón Sartori, Christian Blum, Filippo Bistaffa +1
Since the rise of Large Language Models (LLMs) a couple of years ago, researchers in metaheuristics (MHs) have wondered how to use their power in a beneficial way within their algo…
Large Language Models for the Automated Analysis of Optimization Algorithms
Camilo Chacón Sartori, Christian Blum, Gabriela Ochoa
The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs…