Large Language Models (LLMs), built upon Transformer-based architectures with massive
pretraining on diverse data, have not only revolutionized natural language processing but …

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex
combinatorial optimization problems. However, EAs often demand carefully-designed …

The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain
experts to engage in trial-and-error heuristic design. The long-standing endeavor of design …

Large Transformer models are capable of implementing a plethora of so-called in-context
learning algorithms. These include gradient descent, classification, sequence completion …

Abstract Large Language Models (LLMs) have demonstrated remarkable reasoning
abilities, prompting interest in their application as black-box optimizers. This paper asserts …

Large language models (LLMs) have received considerable attention recently due to their
outstanding comprehension and reasoning capabilities, leading to great progress in many …

It is often very tedious for human experts to design efficient algorithms. Recently, we have
proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for …

Automated heuristic design (AHD) has gained considerable attention for its potential to
automate the development of effective heuristics. The recent advent of large language …

This article pursues the insight that language models naturally enable an intelligent variation
operator similar in spirit to evolutionary crossover. In particular, language models of …

Optimization can be found in many real-life applications. Designing an effective algorithm for
a specific optimization problem typically requires a tedious amount of effort from human …